sp
/
tempest
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Final touches on cuda nondeterministic linear equation solver & modelchecker 

Former-commit-id: c549ae0401
tempestpy_adaptions
David_Korzeniewski 10 years ago
parent
b623384dda
commit
8ebc0e4640
27 changed files with 70 additions and 2663 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 24
      CMakeLists.txt
  2. 64
      resources/cudaForStorm/CMakeAlignmentCheck.cpp
  3. 31
      resources/cudaForStorm/CMakeFloatAlignmentCheck.cpp
  4. 294
      resources/cudaForStorm/CMakeLists.txt
  5. 6
      resources/cudaForStorm/srcCuda/allCudaKernels.h
  6. 0
      resources/cudaForStorm/srcCuda/bandWidth.cu
  7. 0
      resources/cudaForStorm/srcCuda/bandWidth.h
  8. 286
      resources/cudaForStorm/srcCuda/basicAdd.cu
  9. 9
      resources/cudaForStorm/srcCuda/basicAdd.h
  10. 879
      resources/cudaForStorm/srcCuda/basicValueIteration.cu
  11. 107
      resources/cudaForStorm/srcCuda/basicValueIteration.h
  12. 19
      resources/cudaForStorm/srcCuda/cudaForStorm.h
  13. 49
      resources/cudaForStorm/srcCuda/cuspExtension.h
  14. 361
      resources/cudaForStorm/srcCuda/cuspExtensionDouble.h
  15. 375
      resources/cudaForStorm/srcCuda/cuspExtensionFloat.h
  16. 39
      resources/cudaForStorm/srcCuda/kernelSwitchTest.cu
  17. 1
      resources/cudaForStorm/srcCuda/kernelSwitchTest.h
  18. 33
      resources/cudaForStorm/srcCuda/utility.cu
  19. 12
      resources/cudaForStorm/srcCuda/utility.h
  20. 28
      resources/cudaForStorm/srcCuda/version.cu
  21. 16
      resources/cudaForStorm/srcCuda/version.h
  22. 21
      resources/cudaForStorm/storm-cudaplugin-config.h.in
  23. 4
      src/modelchecker/prctl/TopologicalValueIterationMdpPrctlModelChecker.h
  24. 6
      src/solver/TopologicalValueIterationNondeterministicLinearEquationSolver.cpp
  25. 2
      src/solver/TopologicalValueIterationNondeterministicLinearEquationSolver.h
  26. 59
      src/utility/cli.h
  27. 8
      test/functional/modelchecker/TopologicalValueIterationMdpPrctlModelCheckerTest.cpp

24
CMakeLists.txt
View File

@ -27,7 +27,6 @@ option(STORM_USE_INTELTBB "Sets whether the Intel TBB libraries should be used."
option(STORM_USE_COTIRE "Sets whether Cotire should be used (for building precompiled headers)." OFF) option(STORM_USE_COTIRE "Sets whether Cotire should be used (for building precompiled headers)." OFF)
option(LINK_LIBCXXABI "Sets whether libc++abi should be linked." OFF) option(LINK_LIBCXXABI "Sets whether libc++abi should be linked." OFF)
option(USE_LIBCXX "Sets whether the standard library is libc++." OFF) option(USE_LIBCXX "Sets whether the standard library is libc++." OFF)
option(STORM_USE_CUDAFORSTORM "Sets whether StoRM is built with its CUDA extension." OFF)
set(GUROBI_ROOT "" CACHE STRING "The root directory of Gurobi (if available).") set(GUROBI_ROOT "" CACHE STRING "The root directory of Gurobi (if available).")
set(Z3_ROOT "" CACHE STRING "The root directory of Z3 (if available).") set(Z3_ROOT "" CACHE STRING "The root directory of Z3 (if available).")
set(CUDA_ROOT "" CACHE STRING "The root directory of CUDA.") set(CUDA_ROOT "" CACHE STRING "The root directory of CUDA.")
@ -227,11 +226,7 @@ endif()
set(STORM_CPP_GLPK_DEF "define") set(STORM_CPP_GLPK_DEF "define")
# CUDA Defines # CUDA Defines
if (STORM_USE_CUDAFORSTORM)
set(STORM_CPP_CUDAFORSTORM_DEF "define")
else()
set(STORM_CPP_CUDAFORSTORM_DEF "undef")
endif()
set(STORM_CPP_CUDAFORSTORM_DEF "undef")
# Z3 Defines # Z3 Defines
if (ENABLE_Z3) if (ENABLE_Z3)
@ -492,9 +487,6 @@ else(GMP_FOUND)
endif(ENABLE_MSAT) endif(ENABLE_MSAT)
endif(GMP_FOUND) endif(GMP_FOUND)
if (STORM_USE_CUDAFORSTORM)
link_directories("${PROJECT_BINARY_DIR}/cudaForStorm/lib")
endif()
if ((NOT Boost_LIBRARY_DIRS) OR ("${Boost_LIBRARY_DIRS}" STREQUAL "")) if ((NOT Boost_LIBRARY_DIRS) OR ("${Boost_LIBRARY_DIRS}" STREQUAL ""))
set(Boost_LIBRARY_DIRS "${Boost_INCLUDE_DIRS}/stage/lib") set(Boost_LIBRARY_DIRS "${Boost_INCLUDE_DIRS}/stage/lib")
endif () endif ()
@ -529,20 +521,6 @@ target_link_libraries(storm ${Boost_LIBRARIES})
#message(STATUS "BOOST_INCLUDE_DIRS is ${Boost_INCLUDE_DIRS}") #message(STATUS "BOOST_INCLUDE_DIRS is ${Boost_INCLUDE_DIRS}")
#message(STATUS "BOOST_LIBRARY_DIRS is ${Boost_LIBRARY_DIRS}") #message(STATUS "BOOST_LIBRARY_DIRS is ${Boost_LIBRARY_DIRS}")
#############################################################
##
## CUDA For Storm
##
#############################################################
if (STORM_USE_CUDAFORSTORM)
message (STATUS "StoRM - Linking with CudaForStorm")
include_directories("${PROJECT_BINARY_DIR}/cudaForStorm/include")
include_directories("${PROJECT_SOURCE_DIR}/resources/cudaForStorm")
target_link_libraries(storm cudaForStorm)
target_link_libraries(storm-functional-tests cudaForStorm)
target_link_libraries(storm-performance-tests cudaForStorm)
endif(STORM_USE_CUDAFORSTORM)
############################################################# #############################################################
## ##
## CUDD ## CUDD

64
resources/cudaForStorm/CMakeAlignmentCheck.cpp
View File

@ -1,64 +0,0 @@
/*
* This is component of StoRM - Cuda Plugin to check whether type alignment matches the assumptions done while optimizing the code.
*/
#include <cstdint>
#include <utility>
#include <vector>
#define CONTAINER_SIZE 100ul
template <typename IndexType, typename ValueType>
int checkForAlignmentOfPairTypes(size_t containerSize, IndexType const firstValue, ValueType const secondValue) {
std::vector<std::pair<IndexType, ValueType>>* myVector = new std::vector<std::pair<IndexType, ValueType>>();
for (size_t i = 0; i < containerSize; ++i) {
myVector->push_back(std::make_pair(firstValue, secondValue));
}
size_t myVectorSize = myVector->size();
IndexType* firstStart = &(myVector->at(0).first);
IndexType* firstEnd = &(myVector->at(myVectorSize - 1).first);
ValueType* secondStart = &(myVector->at(0).second);
ValueType* secondEnd = &(myVector->at(myVectorSize - 1).second);
size_t startOffset = reinterpret_cast<size_t>(secondStart) - reinterpret_cast<size_t>(firstStart);
size_t endOffset = reinterpret_cast<size_t>(secondEnd) - reinterpret_cast<size_t>(firstEnd);
size_t firstOffset = reinterpret_cast<size_t>(firstEnd) - reinterpret_cast<size_t>(firstStart);
size_t secondOffset = reinterpret_cast<size_t>(secondEnd) - reinterpret_cast<size_t>(secondStart);
delete myVector;
myVector = nullptr;
if (myVectorSize != containerSize) {
return -2;
}
// Check for alignment:
// Requirement is that the pairs are aligned like: first, second, first, second, first, second, ...
if (sizeof(IndexType) != sizeof(ValueType)) {
return -3;
}
if (startOffset != sizeof(IndexType)) {
return -4;
}
if (endOffset != sizeof(IndexType)) {
return -5;
}
if (firstOffset != ((sizeof(IndexType) + sizeof(ValueType)) * (myVectorSize - 1))) {
return -6;
}
if (secondOffset != ((sizeof(IndexType) + sizeof(ValueType)) * (myVectorSize - 1))) {
return -7;
}
return 0;
}
int main(int argc, char* argv[]) {
int result = 0;
result = checkForAlignmentOfPairTypes<uint_fast64_t, double>(CONTAINER_SIZE, 42, 3.14);
if (result != 0) {
return result;
}
return 0;
}

31
resources/cudaForStorm/CMakeFloatAlignmentCheck.cpp
View File

@ -1,31 +0,0 @@
/*
* This is component of StoRM - Cuda Plugin to check whether a pair of uint_fast64_t and float gets auto-aligned to match 64bit boundaries
*/
#include <cstdint>
#include <utility>
#include <vector>
#define CONTAINER_SIZE 100ul
int main(int argc, char* argv[]) {
int result = 0;
std::vector<std::pair<uint_fast64_t, float>> myVector;
for (size_t i = 0; i < CONTAINER_SIZE; ++i) {
myVector.push_back(std::make_pair(i, 42.12345f * i));
}
char* firstUintPointer = reinterpret_cast<char*>(&(myVector.at(0).first));
char* secondUintPointer = reinterpret_cast<char*>(&(myVector.at(1).first));
ptrdiff_t uintDiff = secondUintPointer - firstUintPointer;
if (uintDiff == (2 * sizeof(uint_fast64_t))) {
result = 2;
} else if (uintDiff == (sizeof(uint_fast64_t) + sizeof(float))) {
result = 3;
} else {
result = -5;
}
return result;
}

294
resources/cudaForStorm/CMakeLists.txt
View File

@ -1,294 +0,0 @@
cmake_minimum_required (VERSION 2.8.6)
# Set project name
project (cudaForStorm CXX C)
# Set the version number
set (STORM_CPP_VERSION_MAJOR 1)
set (STORM_CPP_VERSION_MINOR 0)
# Add base folder for better inclusion paths
include_directories("${PROJECT_SOURCE_DIR}")
include_directories("${PROJECT_SOURCE_DIR}/src")
message(STATUS "StoRM (CudaPlugin) - CUDA_PATH is ${CUDA_PATH} or $ENV{CUDA_PATH}")
#############################################################
##
## CMake options of StoRM
##
#############################################################
option(CUDAFORSTORM_DEBUG "Sets whether the DEBUG mode is used" ON)
option(LINK_LIBCXXABI "Sets whether libc++abi should be linked." OFF)
option(USE_LIBCXX "Sets whether the standard library is libc++." OFF)
set(ADDITIONAL_INCLUDE_DIRS "" CACHE STRING "Additional directories added to the include directories.")
set(ADDITIONAL_LINK_DIRS "" CACHE STRING "Additional directories added to the link directories.")
set(STORM_LIB_INSTALL_DIR "${PROJECT_SOURCE_DIR}/../../build/cudaForStorm" CACHE STRING "The Build directory of storm, where the library files should be installed to (if available).")
#############################################################
##
## Inclusion of required libraries
##
#############################################################
# Add the resources/cmake folder to Module Search Path for FindTBB.cmake
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PROJECT_SOURCE_DIR}/../cmake/")
# Set the hint for CUSP
set(CUSP_HINT "${PROJECT_SOURCE_DIR}/../3rdparty/cusplibrary")
find_package(CUDA REQUIRED)
find_package(Cusp REQUIRED)
find_package(Doxygen REQUIRED)
find_package(Thrust REQUIRED)
# If the DEBUG option was turned on, we will target a debug version and a release version otherwise
if (CUDAFORSTORM_DEBUG)
set (CMAKE_BUILD_TYPE "DEBUG")
else()
set (CMAKE_BUILD_TYPE "RELEASE")
endif()
message(STATUS "StoRM (CudaPlugin) - Building ${CMAKE_BUILD_TYPE} version.")
message(STATUS "StoRM (CudaPlugin) - CMAKE_BUILD_TYPE: ${CMAKE_BUILD_TYPE}")
message(STATUS "StoRM (CudaPlugin) - CMAKE_BUILD_TYPE (ENV): $ENV{CMAKE_BUILD_TYPE}")
#############################################################
##
## CUDA Options
##
#############################################################
SET (CUDA_VERBOSE_BUILD ON CACHE BOOL "nvcc verbose" FORCE)
set(CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE ON)
set(BUILD_SHARED_LIBS OFF)
set(CUDA_SEPARABLE_COMPILATION ON)
#set(CUDA_NVCC_FLAGS "-arch=sm_30")
# Because the FindCUDA.cmake file has a path related bug, two folders have to be present
file(MAKE_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/cudaForStorm.dir/Debug")
file(MAKE_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/cudaForStorm.dir/Release")
#############################################################
##
## Compiler specific settings and definitions
##
#############################################################
if(CMAKE_COMPILER_IS_GNUCC)
message(STATUS "StoRM (CudaPlugin) - Using Compiler Configuration: GCC")
# Set standard flags for GCC
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -funroll-loops")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -pedantic")
elseif(MSVC)
message(STATUS "StoRM (CudaPlugin) - Using Compiler Configuration: MSVC")
# required for GMM to compile, ugly error directive in their code
add_definitions(/D_SCL_SECURE_NO_DEPRECATE /D_CRT_SECURE_NO_WARNINGS)
# required as the PRCTL Parser bloats object files (COFF) beyond their maximum size (see http://msdn.microsoft.com/en-us/library/8578y171(v=vs.110).aspx)
add_definitions(/bigobj)
# required by GTest and PrismGrammar::createIntegerVariable
add_definitions(/D_VARIADIC_MAX=10)
# Windows.h breaks GMM in gmm_except.h because of its macro definition for min and max
add_definitions(/DNOMINMAX)
else(CLANG)
message(STATUS "StoRM (CudaPlugin) - Using Compiler Configuration: Clang (LLVM)")
# As CLANG is not set as a variable, we need to set it in case we have not matched another compiler.
set (CLANG ON)
# Set standard flags for clang
set (CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -funroll-loops -O3")
if(UNIX AND NOT APPLE AND NOT USE_LIBCXX)
set(CLANG_STDLIB libstdc++)
message(STATUS "StoRM (CudaPlugin) - Linking against libstdc++")
else()
set(CLANG_STDLIB libc++)
message(STATUS "StoRM (CudaPlugin) - Linking against libc++")
# Set up some Xcode specific settings
set(CMAKE_XCODE_ATTRIBUTE_CLANG_CXX_LANGUAGE_STANDARD "c++11")
set(CMAKE_XCODE_ATTRIBUTE_CLANG_CXX_LIBRARY "libc++")
endif()
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -stdlib=${CLANG_STDLIB} -Wall -pedantic -Wno-unused-variable -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -ftemplate-depth=1024")
set (CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -g")
endif()
#############################################################
##
## CMake-generated Config File for StoRM
##
#############################################################
# Test for type alignment
try_run(STORM_CUDA_RUN_RESULT_TYPEALIGNMENT STORM_CUDA_COMPILE_RESULT_TYPEALIGNMENT
${PROJECT_BINARY_DIR} "${PROJECT_SOURCE_DIR}/CMakeAlignmentCheck.cpp"
COMPILE_OUTPUT_VARIABLE OUTPUT_TEST_VAR
)
if(NOT STORM_CUDA_COMPILE_RESULT_TYPEALIGNMENT)
message(FATAL_ERROR "StoRM (CudaPlugin) - Could not test type alignment, there was an Error while compiling the file ${PROJECT_SOURCE_DIR}/CMakeAlignmentCheck.cpp: ${OUTPUT_TEST_VAR}")
elseif(STORM_CUDA_RUN_RESULT_TYPEALIGNMENT EQUAL 0)
message(STATUS "StoRM (CudaPlugin) - Result of Type Alignment Check: OK.")
else()
message(FATAL_ERROR "StoRM (CudaPlugin) - Result of Type Alignment Check: FAILED (Code ${STORM_CUDA_RUN_RESULT_TYPEALIGNMENT})")
endif()
# Test for Float 64bit Alignment
try_run(STORM_CUDA_RUN_RESULT_FLOATALIGNMENT STORM_CUDA_COMPILE_RESULT_FLOATALIGNMENT
${PROJECT_BINARY_DIR} "${PROJECT_SOURCE_DIR}/CMakeFloatAlignmentCheck.cpp"
COMPILE_OUTPUT_VARIABLE OUTPUT_TEST_VAR
)
if(NOT STORM_CUDA_COMPILE_RESULT_FLOATALIGNMENT)
message(FATAL_ERROR "StoRM (CudaPlugin) - Could not test float type alignment, there was an Error while compiling the file ${PROJECT_SOURCE_DIR}/CMakeFloatAlignmentCheck.cpp: ${OUTPUT_TEST_VAR}")
elseif(STORM_CUDA_RUN_RESULT_FLOATALIGNMENT EQUAL 2)
message(STATUS "StoRM (CudaPlugin) - Result of Float Type Alignment Check: 64bit alignment active.")
set(STORM_CUDAPLUGIN_FLOAT_64BIT_ALIGN_DEF "define")
elseif(STORM_CUDA_RUN_RESULT_FLOATALIGNMENT EQUAL 3)
message(STATUS "StoRM (CudaPlugin) - Result of Float Type Alignment Check: 64bit alignment disabled.")
set(STORM_CUDAPLUGIN_FLOAT_64BIT_ALIGN_DEF "undef")
else()
message(FATAL_ERROR "StoRM (CudaPlugin) - Result of Float Type Alignment Check: FAILED (Code ${STORM_CUDA_RUN_RESULT_FLOATALIGNMENT})")
endif()
#
# Make a version file containing the current version from git.
#
include(GetGitRevisionDescription)
git_describe_checkout(STORM_GIT_VERSION_STRING)
# Parse the git Tag into variables
string(REGEX REPLACE "^([0-9]+)\\..*" "\\1" STORM_CUDAPLUGIN_VERSION_MAJOR "${STORM_GIT_VERSION_STRING}")
string(REGEX REPLACE "^[0-9]+\\.([0-9]+).*" "\\1" STORM_CUDAPLUGIN_VERSION_MINOR "${STORM_GIT_VERSION_STRING}")
string(REGEX REPLACE "^[0-9]+\\.[0-9]+\\.([0-9]+).*" "\\1" STORM_CUDAPLUGIN_VERSION_PATCH "${STORM_GIT_VERSION_STRING}")
string(REGEX REPLACE "^[0-9]+\\.[0-9]+\\.[0-9]+\\-([0-9]+)\\-.*" "\\1" STORM_CUDAPLUGIN_VERSION_COMMITS_AHEAD "${STORM_GIT_VERSION_STRING}")
string(REGEX REPLACE "^[0-9]+\\.[0-9]+\\.[0-9]+\\-[0-9]+\\-([a-z0-9]+).*" "\\1" STORM_CUDAPLUGIN_VERSION_HASH "${STORM_GIT_VERSION_STRING}")
string(REGEX REPLACE "^[0-9]+\\.[0-9]+\\.[0-9]+\\-[0-9]+\\-[a-z0-9]+\\-(.*)" "\\1" STORM_CUDAPLUGIN_VERSION_APPENDIX "${STORM_GIT_VERSION_STRING}")
if ("${STORM_CUDAPLUGIN_VERSION_APPENDIX}" MATCHES "^.*dirty.*$")
set(STORM_CUDAPLUGIN_VERSION_DIRTY 1)
else()
set(STORM_CUDAPLUGIN_VERSION_DIRTY 0)
endif()
message(STATUS "StoRM (CudaPlugin) - Version information: ${STORM_CUDAPLUGIN_VERSION_MAJOR}.${STORM_CUDAPLUGIN_VERSION_MINOR}.${STORM_CUDAPLUGIN_VERSION_PATCH} (${STORM_CUDAPLUGIN_VERSION_COMMITS_AHEAD} commits ahead of Tag) build from ${STORM_CUDAPLUGIN_VERSION_HASH} (Dirty: ${STORM_CUDAPLUGIN_VERSION_DIRTY})")
# Configure a header file to pass some of the CMake settings to the source code
configure_file (
"${PROJECT_SOURCE_DIR}/storm-cudaplugin-config.h.in"
"${PROJECT_BINARY_DIR}/include/storm-cudaplugin-config.h"
)
# Add the binary dir include directory for storm-config.h
include_directories("${PROJECT_BINARY_DIR}/include")
# Add the main source directory for includes
include_directories("${PROJECT_SOURCE_DIR}/../../src")
#############################################################
##
## Source file aggregation and clustering
##
#############################################################
file(GLOB_RECURSE CUDAFORSTORM_HEADERS ${PROJECT_SOURCE_DIR}/src/*.h)
file(GLOB_RECURSE CUDAFORSTORM_SOURCES ${PROJECT_SOURCE_DIR}/src/*.cpp)
file(GLOB_RECURSE CUDAFORSTORM_CUDA_SOURCES "${PROJECT_SOURCE_DIR}/srcCuda/*.cu")
file(GLOB_RECURSE CUDAFORSTORM_CUDA_HEADERS "${PROJECT_SOURCE_DIR}/srcCuda/*.h")
# Additional include files like the storm-config.h
file(GLOB_RECURSE CUDAFORSTORM_BUILD_HEADERS ${PROJECT_BINARY_DIR}/include/*.h)
# Group the headers and sources
source_group(main FILES ${CUDAFORSTORM_HEADERS} ${CUDAFORSTORM_SOURCES})
source_group(cuda FILES ${CUDAFORSTORM_CUDA_SOURCES} ${CUDAFORSTORM_CUDA_HEADERS})
# Add custom additional include or link directories
if (ADDITIONAL_INCLUDE_DIRS)
message(STATUS "StoRM (CudaPlugin) - Using additional include directories ${ADDITIONAL_INCLUDE_DIRS}")
include_directories(${ADDITIONAL_INCLUDE_DIRS})
endif(ADDITIONAL_INCLUDE_DIRS)
if (ADDITIONAL_LINK_DIRS)
message(STATUS "StoRM (CudaPlugin) - Using additional link directories ${ADDITIONAL_LINK_DIRS}")
link_directories(${ADDITIONAL_LINK_DIRS})
endif(ADDITIONAL_LINK_DIRS)
#############################################################
##
## Pre executable-creation link_directories setup
##
#############################################################
#############################################################
##
## CUDA
##
#############################################################
#set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --gpu-architecture sm_30)
#target_link_libraries(cudaLibrary ${CUDA_cusparse_LIBRARY})
#ADD_DEPENDENCIES(cudaForStorm cudaLibrary)
#target_link_libraries(cudaForStorm cudaLibrary)
message(STATUS "StoRM (CudaPlugin) - Found CUDA SDK in Version ${CUDA_VERSION_STRING}, sparse lib is ${CUDA_cusparse_LIBRARY}")
include_directories(${CUDA_INCLUDE_DIRS})
#############################################################
##
## CUSP
##
#############################################################
if(CUSP_FOUND)
include_directories(${CUSP_INCLUDE_DIR})
cuda_include_directories(${CUSP_INCLUDE_DIR})
message(STATUS "StoRM (CudaPlugin) - Found CUSP Version ${CUSP_VERSION} in location ${CUSP_INCLUDE_DIR}")
else()
message(FATAL_ERROR "StoRM (CudaPlugin) - Could not find CUSP!")
endif()
#############################################################
##
## Thrust
##
#############################################################
if(THRUST_FOUND)
include_directories(${THRUST_INCLUDE_DIR})
cuda_include_directories(${THRUST_INCLUDE_DIR})
message(STATUS "StoRM (CudaPlugin) - Found Thrust Version ${THRUST_VERSION} in location ${THRUST_INCLUDE_DIR}")
else()
message(FATAL_ERROR "StoRM (CudaPlugin) - Could not find Thrust! Check your CUDA installation.")
endif()
###############################################################################
## #
## Executable Creation #
## #
## All link_directories() calls AND include_directories() calls #
## MUST be made before this point #
## #
###############################################################################
include (GenerateExportHeader)
cuda_add_library(cudaForStorm SHARED
${CUDAFORSTORM_CUDA_SOURCES} ${CUDAFORSTORM_CUDA_HEADERS}
OPTIONS -DSTUFF="" -arch=sm_30
RELEASE -DNDEBUG
DEBUG -g -DDEBUG
)
GENERATE_EXPORT_HEADER( cudaForStorm
BASE_NAME cudaForStorm
EXPORT_MACRO_NAME cudaForStorm_EXPORT
EXPORT_FILE_NAME include/cudaForStorm_Export.h
STATIC_DEFINE cudaForStorm_BUILT_AS_STATIC
)
if (MSVC)
# Add the DebugHelper DLL
set(CMAKE_CXX_STANDARD_LIBRARIES "${CMAKE_CXX_STANDARD_LIBRARIES} Dbghelp.lib")
target_link_libraries(cudaForStorm "Dbghelp.lib")
endif(MSVC)
# Link against libc++abi if requested. May be needed to build on Linux systems using clang.
if (LINK_LIBCXXABI)
message (STATUS "StoRM (CudaPlugin) - Linking against libc++abi.")
target_link_libraries(cudaForStorm "c++abi")
endif(LINK_LIBCXXABI)
# Install Directive
install(TARGETS cudaForStorm DESTINATION "${STORM_LIB_INSTALL_DIR}/lib")
install(FILES "${PROJECT_SOURCE_DIR}/srcCuda/cudaForStorm.h" "${PROJECT_BINARY_DIR}/include/cudaForStorm_Export.h" DESTINATION "${STORM_LIB_INSTALL_DIR}/include")

6
resources/cudaForStorm/srcCuda/allCudaKernels.h
View File

@ -1,6 +0,0 @@
#include "utility.h"
#include "bandWidth.h"
#include "basicAdd.h"
#include "kernelSwitchTest.h"
#include "basicValueIteration.h"
#include "version.h"

0
resources/cudaForStorm/srcCuda/bandWidth.cu
View File

0
resources/cudaForStorm/srcCuda/bandWidth.h
View File

286
resources/cudaForStorm/srcCuda/basicAdd.cu
View File

@ -1,286 +0,0 @@
#include <cuda.h>
#include <stdlib.h>
#include <stdio.h>
#include <chrono>
#include <iostream>
__global__ void cuda_kernel_basicAdd(int a, int b, int *c) {
*c = a + b;
}
__global__ void cuda_kernel_arrayFma(int const * const A, int const * const B, int const * const C, int * const D, int const N) {
// Fused Multiply Add:
// A * B + C => D
/*
*Die Variable i dient für den Zugriff auf das Array. Da jeder Thread die Funktion VecAdd
*ausführt, muss i für jeden Thread unterschiedlich sein. Ansonsten würden unterschiedliche
*Threads auf denselben Index im Array schreiben. blockDim.x ist die Anzahl der Threads der x-Komponente
*des Blocks, blockIdx.x ist die x-Koordinate des aktuellen Blocks und threadIdx.x ist die x-Koordinate des
*Threads, der die Funktion gerade ausführt.
*/
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < N) {
D[i] = A[i] * B[i] + C[i];
}
}
__global__ void cuda_kernel_arrayFmaOptimized(int * const A, int const N, int const M) {
// Fused Multiply Add:
// A * B + C => D
// Layout:
// A B C D A B C D A B C D
int i = blockDim.x * blockIdx.x + threadIdx.x;
if ((i*M) < N) {
for (int j = i*M; j < i*M + M; ++j) {
A[j*4 + 3] = A[j*4] * A[j*4 + 1] + A[j*4 + 2];
}
}
}
extern "C" int cuda_basicAdd(int a, int b) {
int c = 0;
int *dev_c;
cudaMalloc((void**)&dev_c, sizeof(int));
cuda_kernel_basicAdd<<<1, 1>>>(a, b, dev_c);
cudaMemcpy(&c, dev_c, sizeof(int), cudaMemcpyDeviceToHost);
//printf("%d + %d + 42 is %d\n", a, b, c);
cudaFree(dev_c);
return c;
}
void cpp_cuda_bandwidthTest(int entryCount, int N) {
// Size of the Arrays
size_t arraySize = entryCount * sizeof(int);
int* deviceIntArray;
int* hostIntArray = new int[arraySize];
// Allocate space on the device
auto start_time = std::chrono::high_resolution_clock::now();
for (int i = 0; i < N; ++i) {
if (cudaMalloc((void**)&deviceIntArray, arraySize) != cudaSuccess) {
std::cout << "Error in cudaMalloc while allocating " << arraySize << " Bytes!" << std::endl;
delete[] hostIntArray;
return;
}
// Free memory on device
if (cudaFree(deviceIntArray) != cudaSuccess) {
std::cout << "Error in cudaFree!" << std::endl;
delete[] hostIntArray;
return;
}
}
auto end_time = std::chrono::high_resolution_clock::now();
auto copyTime = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count();
double mBytesPerSecond = (((double)(N * arraySize)) / copyTime) * 0.95367431640625;
std::cout << "Allocating the Array " << N << " times took " << copyTime << " Microseconds." << std::endl;
std::cout << "Resulting in " << mBytesPerSecond << " MBytes per Second Allocationspeed." << std::endl;
if (cudaMalloc((void**)&deviceIntArray, arraySize) != cudaSuccess) {
std::cout << "Error in cudaMalloc while allocating " << arraySize << " Bytes for copyTest!" << std::endl;
delete[] hostIntArray;
return;
}
// Prepare data
for (int i = 0; i < N; ++i) {
hostIntArray[i] = i * 333 + 123;
}
// Copy data TO device
start_time = std::chrono::high_resolution_clock::now();
for (int i = 0; i < N; ++i) {
if (cudaMemcpy(deviceIntArray, hostIntArray, arraySize, cudaMemcpyHostToDevice) != cudaSuccess) {
std::cout << "Error in cudaMemcpy while copying " << arraySize << " Bytes to device!" << std::endl;
// Free memory on device
if (cudaFree(deviceIntArray) != cudaSuccess) {
std::cout << "Error in cudaFree!" << std::endl;
}
delete[] hostIntArray;
return;
}
}
end_time = std::chrono::high_resolution_clock::now();
copyTime = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count();
mBytesPerSecond = (((double)(N * arraySize)) / copyTime) * 0.95367431640625;
std::cout << "Copying the Array " << N << " times took " << copyTime << " Microseconds." << std::endl;
std::cout << "Resulting in " << mBytesPerSecond << " MBytes per Second TO device." << std::endl;
// Copy data FROM device
start_time = std::chrono::high_resolution_clock::now();
for (int i = 0; i < N; ++i) {
if (cudaMemcpy(hostIntArray, deviceIntArray, arraySize, cudaMemcpyDeviceToHost) != cudaSuccess) {
std::cout << "Error in cudaMemcpy while copying " << arraySize << " Bytes to host!" << std::endl;
// Free memory on device
if (cudaFree(deviceIntArray) != cudaSuccess) {
std::cout << "Error in cudaFree!" << std::endl;
}
delete[] hostIntArray;
return;
}
}
end_time = std::chrono::high_resolution_clock::now();
copyTime = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count();
mBytesPerSecond = (((double)(N * arraySize)) / copyTime) * 0.95367431640625;
std::cout << "Copying the Array " << N << " times took " << copyTime << " Microseconds." << std::endl;
std::cout << "Resulting in " << mBytesPerSecond << " MBytes per Second FROM device." << std::endl;
// Free memory on device
if (cudaFree(deviceIntArray) != cudaSuccess) {
std::cout << "Error in cudaFree!" << std::endl;
}
delete[] hostIntArray;
}
extern "C" void cuda_arrayFma(int const * const A, int const * const B, int const * const C, int * const D, int const N) {
// Size of the Arrays
size_t arraySize = N * sizeof(int);
int* deviceIntArrayA;
int* deviceIntArrayB;
int* deviceIntArrayC;
int* deviceIntArrayD;
// Allocate space on the device
if (cudaMalloc((void**)&deviceIntArrayA, arraySize) != cudaSuccess) {
printf("Error in cudaMalloc1!\n");
return;
}
if (cudaMalloc((void**)&deviceIntArrayB, arraySize) != cudaSuccess) {
printf("Error in cudaMalloc2!\n");
cudaFree(deviceIntArrayA);
return;
}
if (cudaMalloc((void**)&deviceIntArrayC, arraySize) != cudaSuccess) {
printf("Error in cudaMalloc3!\n");
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
return;
}
if (cudaMalloc((void**)&deviceIntArrayD, arraySize) != cudaSuccess) {
printf("Error in cudaMalloc4!\n");
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
cudaFree(deviceIntArrayC);
return;
}
// Copy data TO device
if (cudaMemcpy(deviceIntArrayA, A, arraySize, cudaMemcpyHostToDevice) != cudaSuccess) {
printf("Error in cudaMemcpy!\n");
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
cudaFree(deviceIntArrayC);
cudaFree(deviceIntArrayD);
return;
}
if (cudaMemcpy(deviceIntArrayB, B, arraySize, cudaMemcpyHostToDevice) != cudaSuccess) {
printf("Error in cudaMemcpy!\n");
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
cudaFree(deviceIntArrayC);
cudaFree(deviceIntArrayD);
return;
}
if (cudaMemcpy(deviceIntArrayC, C, arraySize, cudaMemcpyHostToDevice) != cudaSuccess) {
printf("Error in cudaMemcpy!\n");
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
cudaFree(deviceIntArrayC);
cudaFree(deviceIntArrayD);
return;
}
// Festlegung der Threads pro Block
int threadsPerBlock = 512;
// Es werden soviele Blöcke benötigt, dass alle Elemente der Vektoren abgearbeitet werden können
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
// Run kernel
cuda_kernel_arrayFma<<<blocksPerGrid, threadsPerBlock>>>(deviceIntArrayA, deviceIntArrayB, deviceIntArrayC, deviceIntArrayD, N);
// Copy data FROM device
if (cudaMemcpy(D, deviceIntArrayD, arraySize, cudaMemcpyDeviceToHost) != cudaSuccess) {
printf("Error in cudaMemcpy!\n");
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
cudaFree(deviceIntArrayC);
cudaFree(deviceIntArrayD);
return;
}
// Free memory on device
cudaFree(deviceIntArrayA);
cudaFree(deviceIntArrayB);
cudaFree(deviceIntArrayC);
cudaFree(deviceIntArrayD);
}
extern "C" void cuda_arrayFmaOptimized(int * const A, int const N, int const M) {
// Size of the Arrays
size_t arraySize = N * sizeof(int) * 4;
int* deviceIntArrayA;
// Allocate space on the device
if (cudaMalloc((void**)&deviceIntArrayA, arraySize) != cudaSuccess) {
printf("Error in cudaMalloc1!\n");
return;
}
#define ONFAILFREE0() do { } while(0)
#define ONFAILFREE1(a) do { cudaFree(a); } while(0)
#define ONFAILFREE2(a, b) do { cudaFree(a); cudaFree(b); } while(0)
#define ONFAILFREE3(a, b, c) do { cudaFree(a); cudaFree(b); cudaFree(c); } while(0)
#define ONFAILFREE4(a, b, c, d) do { cudaFree(a); cudaFree(b); cudaFree(c); cudaFree(d); } while(0)
#define CHECKED_CUDA_CALL(func__, freeArgs, ...) do { int retCode = cuda##func__ (__VA_ARGS__); if (retCode != cudaSuccess) { freeArgs; printf("Error in func__!\n"); return; } } while(0)
// Copy data TO device
CHECKED_CUDA_CALL(Memcpy, ONFAILFREE1(deviceIntArrayA), deviceIntArrayA, A, arraySize, cudaMemcpyHostToDevice);
/*if (cudaMemcpy(deviceIntArrayA, A, arraySize, cudaMemcpyHostToDevice) != cudaSuccess) {
printf("Error in cudaMemcpy!\n");
cudaFree(deviceIntArrayA);
return;
}*/
// Festlegung der Threads pro Block
int threadsPerBlock = 512;
// Es werden soviele Blöcke benötigt, dass alle Elemente der Vektoren abgearbeitet werden können
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
// Run kernel
cuda_kernel_arrayFmaOptimized<<<blocksPerGrid, threadsPerBlock>>>(deviceIntArrayA, N, M);
// Copy data FROM device
if (cudaMemcpy(A, deviceIntArrayA, arraySize, cudaMemcpyDeviceToHost) != cudaSuccess) {
printf("Error in cudaMemcpy!\n");
cudaFree(deviceIntArrayA);
return;
}
// Free memory on device
if (cudaFree(deviceIntArrayA) != cudaSuccess) {
printf("Error in cudaFree!\n");
return;
}
}
extern "C" void cuda_arrayFmaHelper(int const * const A, int const * const B, int const * const C, int * const D, int const N) {
for (int i = 0; i < N; ++i) {
D[i] = A[i] * B[i] + C[i];
}
}
extern "C" void cuda_arrayFmaOptimizedHelper(int * const A, int const N) {
for (int i = 0; i < N; i += 4) {
A[i+3] = A[i] * A[i+1] + A[i+2];
}
}

9
resources/cudaForStorm/srcCuda/basicAdd.h
View File

@ -1,9 +0,0 @@
extern "C" int cuda_basicAdd(int a, int b);
extern "C" void cuda_arrayFmaOptimized(int * const A, int const N, int const M);
extern "C" void cuda_arrayFmaOptimizedHelper(int * const A, int const N);
extern "C" void cuda_arrayFma(int const * const A, int const * const B, int const * const C, int * const D, int const N);
extern "C" void cuda_arrayFmaHelper(int const * const A, int const * const B, int const * const C, int * const D, int const N);
void cpp_cuda_bandwidthTest(int entryCount, int N);

879
resources/cudaForStorm/srcCuda/basicValueIteration.cu
View File

@ -1,879 +0,0 @@
#include "basicValueIteration.h"
#define CUSP_USE_TEXTURE_MEMORY
#include <iostream>
#include <chrono>
#include <cuda_runtime.h>
#include "cusparse_v2.h"
#include "utility.h"
#include "cuspExtension.h"
#include <thrust/transform.h>
#include <thrust/device_ptr.h>
#include <thrust/functional.h>
#include "storm-cudaplugin-config.h"
#ifdef DEBUG
#define CUDA_CHECK_ALL_ERRORS() do { cudaError_t errSync = cudaGetLastError(); cudaError_t errAsync = cudaDeviceSynchronize(); if (errSync != cudaSuccess) { std::cout << "(DLL) Sync kernel error: " << cudaGetErrorString(errSync) << " (Code: " << errSync << ") in Line " << __LINE__ << std::endl; } if (errAsync != cudaSuccess) { std::cout << "(DLL) Async kernel error: " << cudaGetErrorString(errAsync) << " (Code: " << errAsync << ") in Line " << __LINE__ << std::endl; } } while(false)
#else
#define CUDA_CHECK_ALL_ERRORS() do {} while (false)
#endif
template<typename T, bool Relative>
struct equalModuloPrecision : public thrust::binary_function<T,T,T>
{
__host__ __device__ T operator()(const T &x, const T &y) const
{
if (Relative) {
if (y == 0) {
return ((x >= 0) ? (x) : (-x));
}
const T result = (x - y) / y;
return ((result >= 0) ? (result) : (-result));
} else {
const T result = (x - y);
return ((result >= 0) ? (result) : (-result));
}
}
};
template<typename IndexType, typename ValueType>
void exploadVector(std::vector<std::pair<IndexType, ValueType>> const& inputVector, std::vector<IndexType>& indexVector, std::vector<ValueType>& valueVector) {
indexVector.reserve(inputVector.size());
valueVector.reserve(inputVector.size());
for (size_t i = 0; i < inputVector.size(); ++i) {
indexVector.push_back(inputVector.at(i).first);
valueVector.push_back(inputVector.at(i).second);
}
}
// TEMPLATE VERSION
template <bool Minimize, bool Relative, typename IndexType, typename ValueType>
bool basicValueIteration_mvReduce(uint_fast64_t const maxIterationCount, double const precision, std::vector<IndexType> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<ValueType>> const& columnIndicesAndValues, std::vector<ValueType>& x, std::vector<ValueType> const& b, std::vector<IndexType> const& nondeterministicChoiceIndices, size_t& iterationCount) {
//std::vector<IndexType> matrixColumnIndices;
//std::vector<ValueType> matrixValues;
//exploadVector<IndexType, ValueType>(columnIndicesAndValues, matrixColumnIndices, matrixValues);
bool errorOccured = false;
IndexType* device_matrixRowIndices = nullptr;
ValueType* device_matrixColIndicesAndValues = nullptr;
ValueType* device_x = nullptr;
ValueType* device_xSwap = nullptr;
ValueType* device_b = nullptr;
ValueType* device_multiplyResult = nullptr;
IndexType* device_nondeterministicChoiceIndices = nullptr;
#ifdef DEBUG
std::cout.sync_with_stdio(true);
std::cout << "(DLL) Entering CUDA Function: basicValueIteration_mvReduce" << std::endl;
std::cout << "(DLL) Device has " << getTotalCudaMemory() << " Bytes of Memory with " << getFreeCudaMemory() << "Bytes free (" << (static_cast<double>(getFreeCudaMemory()) / static_cast<double>(getTotalCudaMemory())) * 100 << "%)." << std::endl;
size_t memSize = sizeof(IndexType) * matrixRowIndices.size() + sizeof(IndexType) * columnIndicesAndValues.size() * 2 + sizeof(ValueType) * x.size() + sizeof(ValueType) * x.size() + sizeof(ValueType) * b.size() + sizeof(ValueType) * b.size() + sizeof(IndexType) * nondeterministicChoiceIndices.size();
std::cout << "(DLL) We will allocate " << memSize << " Bytes." << std::endl;
#endif
const IndexType matrixRowCount = matrixRowIndices.size() - 1;
const IndexType matrixColCount = nondeterministicChoiceIndices.size() - 1;
const IndexType matrixNnzCount = columnIndicesAndValues.size();
cudaError_t cudaMallocResult;
bool converged = false;
iterationCount = 0;
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_matrixRowIndices), sizeof(IndexType) * (matrixRowCount + 1));
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Matrix Row Indices, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
#define STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT_VALUE true
#else
#define STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT_VALUE false
#endif
if (sizeof(ValueType) == sizeof(float) && STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT_VALUE) {
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_matrixColIndicesAndValues), sizeof(IndexType) * matrixNnzCount + sizeof(IndexType) * matrixNnzCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Matrix Column Indices and Values, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
} else {
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_matrixColIndicesAndValues), sizeof(IndexType) * matrixNnzCount + sizeof(ValueType) * matrixNnzCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Matrix Column Indices and Values, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_x), sizeof(ValueType) * matrixColCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector x, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_xSwap), sizeof(ValueType) * matrixColCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector x swap, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_b), sizeof(ValueType) * matrixRowCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector b, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_multiplyResult), sizeof(ValueType) * matrixRowCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector multiplyResult, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_nondeterministicChoiceIndices), sizeof(IndexType) * (matrixColCount + 1));
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Nondeterministic Choice Indices, Error Code " << cudaMallocResult << "." << std::endl;
errorOccured = true;
goto cleanup;
}
#ifdef DEBUG
std::cout << "(DLL) Finished allocating memory." << std::endl;
#endif
// Memory allocated, copy data to device
cudaError_t cudaCopyResult;
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_matrixRowIndices, matrixRowIndices.data(), sizeof(IndexType) * (matrixRowCount + 1), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Matrix Row Indices, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
// Copy all data as floats are expanded to 64bits :/
if (sizeof(ValueType) == sizeof(float) && STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT_VALUE) {
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_matrixColIndicesAndValues, columnIndicesAndValues.data(), (sizeof(IndexType) * matrixNnzCount) + (sizeof(IndexType) * matrixNnzCount), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Matrix Column Indices and Values, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
} else {
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_matrixColIndicesAndValues, columnIndicesAndValues.data(), (sizeof(IndexType) * matrixNnzCount) + (sizeof(ValueType) * matrixNnzCount), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Matrix Column Indices and Values, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
}
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_x, x.data(), sizeof(ValueType) * matrixColCount, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector x, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
// Preset the xSwap to zeros...
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemset(device_xSwap, 0, sizeof(ValueType) * matrixColCount);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not zero the Swap Vector x, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_b, b.data(), sizeof(ValueType) * matrixRowCount, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector b, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
// Preset the multiplyResult to zeros...
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemset(device_multiplyResult, 0, sizeof(ValueType) * matrixRowCount);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not zero the multiply Result, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_nondeterministicChoiceIndices, nondeterministicChoiceIndices.data(), sizeof(IndexType) * (matrixColCount + 1), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector b, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
#ifdef DEBUG
std::cout << "(DLL) Finished copying data to GPU memory." << std::endl;
#endif
// Data is on device, start Kernel
while (!converged && iterationCount < maxIterationCount) { // In a sub-area since transfer of control via label evades initialization
cusp::detail::device::storm_cuda_opt_spmv_csr_vector<ValueType>(matrixRowCount, matrixNnzCount, device_matrixRowIndices, device_matrixColIndicesAndValues, device_x, device_multiplyResult);
CUDA_CHECK_ALL_ERRORS();
thrust::device_ptr<ValueType> devicePtrThrust_b(device_b);
thrust::device_ptr<ValueType> devicePtrThrust_multiplyResult(device_multiplyResult);
// Transform: Add multiplyResult + b inplace to multiplyResult
thrust::transform(devicePtrThrust_multiplyResult, devicePtrThrust_multiplyResult + matrixRowCount, devicePtrThrust_b, devicePtrThrust_multiplyResult, thrust::plus<ValueType>());
CUDA_CHECK_ALL_ERRORS();
// Reduce: Reduce multiplyResult to a new x vector
cusp::detail::device::storm_cuda_opt_vector_reduce<Minimize, ValueType>(matrixColCount, matrixRowCount, device_nondeterministicChoiceIndices, device_xSwap, device_multiplyResult);
CUDA_CHECK_ALL_ERRORS();
// Check for convergence
// Transform: x = abs(x - xSwap)/ xSwap
thrust::device_ptr<ValueType> devicePtrThrust_x(device_x);
thrust::device_ptr<ValueType> devicePtrThrust_x_end(device_x + matrixColCount);
thrust::device_ptr<ValueType> devicePtrThrust_xSwap(device_xSwap);
thrust::transform(devicePtrThrust_x, devicePtrThrust_x_end, devicePtrThrust_xSwap, devicePtrThrust_x, equalModuloPrecision<ValueType, Relative>());
CUDA_CHECK_ALL_ERRORS();
// Reduce: get Max over x and check for res < Precision
ValueType maxX = thrust::reduce(devicePtrThrust_x, devicePtrThrust_x_end, -std::numeric_limits<ValueType>::max(), thrust::maximum<ValueType>());
CUDA_CHECK_ALL_ERRORS();
converged = (maxX < precision);
++iterationCount;
// Swap pointers, device_x always contains the most current result
std::swap(device_x, device_xSwap);
}
if (!converged && (iterationCount == maxIterationCount)) {
iterationCount = 0;
errorOccured = true;
}
#ifdef DEBUG
std::cout << "(DLL) Finished kernel execution." << std::endl;
std::cout << "(DLL) Executed " << iterationCount << " of max. " << maxIterationCount << " Iterations." << std::endl;
#endif
// Get x back from the device
cudaCopyResult = cudaMemcpy(x.data(), device_x, sizeof(ValueType) * matrixColCount, cudaMemcpyDeviceToHost);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy back data for result vector x, Error Code " << cudaCopyResult << std::endl;
errorOccured = true;
goto cleanup;
}
#ifdef DEBUG
std::cout << "(DLL) Finished copying result data." << std::endl;
#endif
// All code related to freeing memory and clearing up the device
cleanup:
if (device_matrixRowIndices != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_matrixRowIndices);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Matrix Row Indices, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_matrixRowIndices = nullptr;
}
if (device_matrixColIndicesAndValues != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_matrixColIndicesAndValues);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Matrix Column Indices and Values, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_matrixColIndicesAndValues = nullptr;
}
if (device_x != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_x);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector x, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_x = nullptr;
}
if (device_xSwap != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_xSwap);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector x swap, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_xSwap = nullptr;
}
if (device_b != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_b);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector b, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_b = nullptr;
}
if (device_multiplyResult != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_multiplyResult);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector multiplyResult, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_multiplyResult = nullptr;
}
if (device_nondeterministicChoiceIndices != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_nondeterministicChoiceIndices);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Nondeterministic Choice Indices, Error Code " << cudaFreeResult << "." << std::endl;
errorOccured = true;
}
device_nondeterministicChoiceIndices = nullptr;
}
#ifdef DEBUG
std::cout << "(DLL) Finished cleanup." << std::endl;
#endif
return !errorOccured;
}
template <typename IndexType, typename ValueType>
void basicValueIteration_spmv(uint_fast64_t const matrixColCount, std::vector<IndexType> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<ValueType>> const& columnIndicesAndValues, std::vector<ValueType> const& x, std::vector<ValueType>& b) {
IndexType* device_matrixRowIndices = nullptr;
ValueType* device_matrixColIndicesAndValues = nullptr;
ValueType* device_x = nullptr;
ValueType* device_multiplyResult = nullptr;
#ifdef DEBUG
std::cout.sync_with_stdio(true);
std::cout << "(DLL) Entering CUDA Function: basicValueIteration_spmv" << std::endl;
std::cout << "(DLL) Device has " << getTotalCudaMemory() << " Bytes of Memory with " << getFreeCudaMemory() << "Bytes free (" << (static_cast<double>(getFreeCudaMemory()) / static_cast<double>(getTotalCudaMemory()))*100 << "%)." << std::endl;
size_t memSize = sizeof(IndexType) * matrixRowIndices.size() + sizeof(IndexType) * columnIndicesAndValues.size() * 2 + sizeof(ValueType) * x.size() + sizeof(ValueType) * b.size();
std::cout << "(DLL) We will allocate " << memSize << " Bytes." << std::endl;
#endif
const IndexType matrixRowCount = matrixRowIndices.size() - 1;
const IndexType matrixNnzCount = columnIndicesAndValues.size();
cudaError_t cudaMallocResult;
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_matrixRowIndices), sizeof(IndexType) * (matrixRowCount + 1));
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Matrix Row Indices, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_matrixColIndicesAndValues), sizeof(IndexType) * matrixNnzCount + sizeof(IndexType) * matrixNnzCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Matrix Column Indices And Values, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
#else
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_matrixColIndicesAndValues), sizeof(IndexType) * matrixNnzCount + sizeof(ValueType) * matrixNnzCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Matrix Column Indices And Values, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
#endif
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_x), sizeof(ValueType) * matrixColCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector x, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_multiplyResult), sizeof(ValueType) * matrixRowCount);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector multiplyResult, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
#ifdef DEBUG
std::cout << "(DLL) Finished allocating memory." << std::endl;
#endif
// Memory allocated, copy data to device
cudaError_t cudaCopyResult;
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_matrixRowIndices, matrixRowIndices.data(), sizeof(IndexType) * (matrixRowCount + 1), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Matrix Row Indices, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_matrixColIndicesAndValues, columnIndicesAndValues.data(), (sizeof(IndexType) * matrixNnzCount) + (sizeof(IndexType) * matrixNnzCount), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Matrix Column Indices and Values, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
#else
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_matrixColIndicesAndValues, columnIndicesAndValues.data(), (sizeof(IndexType) * matrixNnzCount) + (sizeof(ValueType) * matrixNnzCount), cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Matrix Column Indices and Values, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
#endif
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_x, x.data(), sizeof(ValueType) * matrixColCount, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector x, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
// Preset the multiplyResult to zeros...
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemset(device_multiplyResult, 0, sizeof(ValueType) * matrixRowCount);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not zero the multiply Result, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
#ifdef DEBUG
std::cout << "(DLL) Finished copying data to GPU memory." << std::endl;
#endif
cusp::detail::device::storm_cuda_opt_spmv_csr_vector<ValueType>(matrixRowCount, matrixNnzCount, device_matrixRowIndices, device_matrixColIndicesAndValues, device_x, device_multiplyResult);
CUDA_CHECK_ALL_ERRORS();
#ifdef DEBUG
std::cout << "(DLL) Finished kernel execution." << std::endl;
#endif
// Get result back from the device
cudaCopyResult = cudaMemcpy(b.data(), device_multiplyResult, sizeof(ValueType) * matrixRowCount, cudaMemcpyDeviceToHost);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy back data for result vector, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
#ifdef DEBUG
std::cout << "(DLL) Finished copying result data." << std::endl;
#endif
// All code related to freeing memory and clearing up the device
cleanup:
if (device_matrixRowIndices != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_matrixRowIndices);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Matrix Row Indices, Error Code " << cudaFreeResult << "." << std::endl;
}
device_matrixRowIndices = nullptr;
}
if (device_matrixColIndicesAndValues != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_matrixColIndicesAndValues);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Matrix Column Indices and Values, Error Code " << cudaFreeResult << "." << std::endl;
}
device_matrixColIndicesAndValues = nullptr;
}
if (device_x != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_x);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector x, Error Code " << cudaFreeResult << "." << std::endl;
}
device_x = nullptr;
}
if (device_multiplyResult != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_multiplyResult);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector multiplyResult, Error Code " << cudaFreeResult << "." << std::endl;
}
device_multiplyResult = nullptr;
}
#ifdef DEBUG
std::cout << "(DLL) Finished cleanup." << std::endl;
#endif
}
template <typename ValueType>
void basicValueIteration_addVectorsInplace(std::vector<ValueType>& a, std::vector<ValueType> const& b) {
ValueType* device_a = nullptr;
ValueType* device_b = nullptr;
const size_t vectorSize = std::max(a.size(), b.size());
cudaError_t cudaMallocResult;
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_a), sizeof(ValueType) * vectorSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector a, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_b), sizeof(ValueType) * vectorSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector b, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
// Memory allocated, copy data to device
cudaError_t cudaCopyResult;
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_a, a.data(), sizeof(ValueType) * vectorSize, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector a, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_b, b.data(), sizeof(ValueType) * vectorSize, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector b, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
do {
// Transform: Add multiplyResult + b inplace to multiplyResult
thrust::device_ptr<ValueType> devicePtrThrust_a(device_a);
thrust::device_ptr<ValueType> devicePtrThrust_b(device_b);
thrust::transform(devicePtrThrust_a, devicePtrThrust_a + vectorSize, devicePtrThrust_b, devicePtrThrust_a, thrust::plus<ValueType>());
CUDA_CHECK_ALL_ERRORS();
} while (false);
// Get result back from the device
cudaCopyResult = cudaMemcpy(a.data(), device_a, sizeof(ValueType) * vectorSize, cudaMemcpyDeviceToHost);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy back data for result vector, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
// All code related to freeing memory and clearing up the device
cleanup:
if (device_a != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_a);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector a, Error Code " << cudaFreeResult << "." << std::endl;
}
device_a = nullptr;
}
if (device_b != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_b);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector b, Error Code " << cudaFreeResult << "." << std::endl;
}
device_b = nullptr;
}
}
template <typename IndexType, typename ValueType, bool Minimize>
void basicValueIteration_reduceGroupedVector(std::vector<ValueType> const& groupedVector, std::vector<IndexType> const& grouping, std::vector<ValueType>& targetVector) {
ValueType* device_groupedVector = nullptr;
IndexType* device_grouping = nullptr;
ValueType* device_target = nullptr;
const size_t groupedSize = groupedVector.size();
const size_t groupingSize = grouping.size();
const size_t targetSize = targetVector.size();
cudaError_t cudaMallocResult;
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_groupedVector), sizeof(ValueType) * groupedSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector groupedVector, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_grouping), sizeof(IndexType) * groupingSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector grouping, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_target), sizeof(ValueType) * targetSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector targetVector, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
// Memory allocated, copy data to device
cudaError_t cudaCopyResult;
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_groupedVector, groupedVector.data(), sizeof(ValueType) * groupedSize, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector groupedVector, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_grouping, grouping.data(), sizeof(IndexType) * groupingSize, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector grouping, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
do {
// Reduce: Reduce multiplyResult to a new x vector
cusp::detail::device::storm_cuda_opt_vector_reduce<Minimize, ValueType>(groupingSize - 1, groupedSize, device_grouping, device_target, device_groupedVector);
CUDA_CHECK_ALL_ERRORS();
} while (false);
// Get result back from the device
cudaCopyResult = cudaMemcpy(targetVector.data(), device_target, sizeof(ValueType) * targetSize, cudaMemcpyDeviceToHost);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy back data for result vector, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
// All code related to freeing memory and clearing up the device
cleanup:
if (device_groupedVector != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_groupedVector);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector groupedVector, Error Code " << cudaFreeResult << "." << std::endl;
}
device_groupedVector = nullptr;
}
if (device_grouping != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_grouping);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector grouping, Error Code " << cudaFreeResult << "." << std::endl;
}
device_grouping = nullptr;
}
if (device_target != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_target);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector target, Error Code " << cudaFreeResult << "." << std::endl;
}
device_target = nullptr;
}
}
template <typename ValueType, bool Relative>
void basicValueIteration_equalModuloPrecision(std::vector<ValueType> const& x, std::vector<ValueType> const& y, ValueType& maxElement) {
ValueType* device_x = nullptr;
ValueType* device_y = nullptr;
const size_t vectorSize = x.size();
cudaError_t cudaMallocResult;
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_x), sizeof(ValueType) * vectorSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector x, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaMallocResult = cudaMalloc(reinterpret_cast<void**>(&device_y), sizeof(ValueType) * vectorSize);
if (cudaMallocResult != cudaSuccess) {
std::cout << "Could not allocate memory for Vector y, Error Code " << cudaMallocResult << "." << std::endl;
goto cleanup;
}
// Memory allocated, copy data to device
cudaError_t cudaCopyResult;
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_x, x.data(), sizeof(ValueType) * vectorSize, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector x, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
CUDA_CHECK_ALL_ERRORS();
cudaCopyResult = cudaMemcpy(device_y, y.data(), sizeof(ValueType) * vectorSize, cudaMemcpyHostToDevice);
if (cudaCopyResult != cudaSuccess) {
std::cout << "Could not copy data for Vector y, Error Code " << cudaCopyResult << std::endl;
goto cleanup;
}
do {
// Transform: x = abs(x - xSwap)/ xSwap
thrust::device_ptr<ValueType> devicePtrThrust_x(device_x);
thrust::device_ptr<ValueType> devicePtrThrust_y(device_y);
thrust::transform(devicePtrThrust_x, devicePtrThrust_x + vectorSize, devicePtrThrust_y, devicePtrThrust_x, equalModuloPrecision<ValueType, Relative>());
CUDA_CHECK_ALL_ERRORS();
// Reduce: get Max over x and check for res < Precision
maxElement = thrust::reduce(devicePtrThrust_x, devicePtrThrust_x + vectorSize, -std::numeric_limits<ValueType>::max(), thrust::maximum<ValueType>());
CUDA_CHECK_ALL_ERRORS();
} while (false);
// All code related to freeing memory and clearing up the device
cleanup:
if (device_x != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_x);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector x, Error Code " << cudaFreeResult << "." << std::endl;
}
device_x = nullptr;
}
if (device_y != nullptr) {
cudaError_t cudaFreeResult = cudaFree(device_y);
if (cudaFreeResult != cudaSuccess) {
std::cout << "Could not free Memory of Vector y, Error Code " << cudaFreeResult << "." << std::endl;
}
device_y = nullptr;
}
}
/*
* Declare and implement all exported functions for these Kernels here
*
*/
void basicValueIteration_spmv_uint64_double(uint_fast64_t const matrixColCount, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<double>> const& columnIndicesAndValues, std::vector<double> const& x, std::vector<double>& b) {
basicValueIteration_spmv<uint_fast64_t, double>(matrixColCount, matrixRowIndices, columnIndicesAndValues, x, b);
}
void basicValueIteration_addVectorsInplace_double(std::vector<double>& a, std::vector<double> const& b) {
basicValueIteration_addVectorsInplace<double>(a, b);
}
void basicValueIteration_reduceGroupedVector_uint64_double_minimize(std::vector<double> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<double>& targetVector) {
basicValueIteration_reduceGroupedVector<uint_fast64_t, double, true>(groupedVector, grouping, targetVector);
}
void basicValueIteration_reduceGroupedVector_uint64_double_maximize(std::vector<double> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<double>& targetVector) {
basicValueIteration_reduceGroupedVector<uint_fast64_t, double, false>(groupedVector, grouping, targetVector);
}
void basicValueIteration_equalModuloPrecision_double_Relative(std::vector<double> const& x, std::vector<double> const& y, double& maxElement) {
basicValueIteration_equalModuloPrecision<double, true>(x, y, maxElement);
}
void basicValueIteration_equalModuloPrecision_double_NonRelative(std::vector<double> const& x, std::vector<double> const& y, double& maxElement) {
basicValueIteration_equalModuloPrecision<double, false>(x, y, maxElement);
}
// Float
void basicValueIteration_spmv_uint64_float(uint_fast64_t const matrixColCount, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<float>> const& columnIndicesAndValues, std::vector<float> const& x, std::vector<float>& b) {
basicValueIteration_spmv<uint_fast64_t, float>(matrixColCount, matrixRowIndices, columnIndicesAndValues, x, b);
}
void basicValueIteration_addVectorsInplace_float(std::vector<float>& a, std::vector<float> const& b) {
basicValueIteration_addVectorsInplace<float>(a, b);
}
void basicValueIteration_reduceGroupedVector_uint64_float_minimize(std::vector<float> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<float>& targetVector) {
basicValueIteration_reduceGroupedVector<uint_fast64_t, float, true>(groupedVector, grouping, targetVector);
}
void basicValueIteration_reduceGroupedVector_uint64_float_maximize(std::vector<float> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<float>& targetVector) {
basicValueIteration_reduceGroupedVector<uint_fast64_t, float, false>(groupedVector, grouping, targetVector);
}
void basicValueIteration_equalModuloPrecision_float_Relative(std::vector<float> const& x, std::vector<float> const& y, float& maxElement) {
basicValueIteration_equalModuloPrecision<float, true>(x, y, maxElement);
}
void basicValueIteration_equalModuloPrecision_float_NonRelative(std::vector<float> const& x, std::vector<float> const& y, float& maxElement) {
basicValueIteration_equalModuloPrecision<float, false>(x, y, maxElement);
}
bool basicValueIteration_mvReduce_uint64_double_minimize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<double>> const& columnIndicesAndValues, std::vector<double>& x, std::vector<double> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount) {
if (relativePrecisionCheck) {
return basicValueIteration_mvReduce<true, true, uint_fast64_t, double>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
} else {
return basicValueIteration_mvReduce<true, false, uint_fast64_t, double>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
}
}
bool basicValueIteration_mvReduce_uint64_double_maximize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<double>> const& columnIndicesAndValues, std::vector<double>& x, std::vector<double> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount) {
if (relativePrecisionCheck) {
return basicValueIteration_mvReduce<false, true, uint_fast64_t, double>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
} else {
return basicValueIteration_mvReduce<false, false, uint_fast64_t, double>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
}
}
bool basicValueIteration_mvReduce_uint64_float_minimize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<float>> const& columnIndicesAndValues, std::vector<float>& x, std::vector<float> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount) {
if (relativePrecisionCheck) {
return basicValueIteration_mvReduce<true, true, uint_fast64_t, float>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
} else {
return basicValueIteration_mvReduce<true, false, uint_fast64_t, float>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
}
}
bool basicValueIteration_mvReduce_uint64_float_maximize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<float>> const& columnIndicesAndValues, std::vector<float>& x, std::vector<float> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount) {
if (relativePrecisionCheck) {
return basicValueIteration_mvReduce<false, true, uint_fast64_t, float>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
} else {
return basicValueIteration_mvReduce<false, false, uint_fast64_t, float>(maxIterationCount, precision, matrixRowIndices, columnIndicesAndValues, x, b, nondeterministicChoiceIndices, iterationCount);
}
}
size_t basicValueIteration_mvReduce_uint64_double_calculateMemorySize(size_t const rowCount, size_t const rowGroupCount, size_t const nnzCount) {
size_t const valueTypeSize = sizeof(double);
size_t const indexTypeSize = sizeof(uint_fast64_t);
/*
IndexType* device_matrixRowIndices = nullptr;
IndexType* device_matrixColIndices = nullptr;
ValueType* device_matrixValues = nullptr;
ValueType* device_x = nullptr;
ValueType* device_xSwap = nullptr;
ValueType* device_b = nullptr;
ValueType* device_multiplyResult = nullptr;
IndexType* device_nondeterministicChoiceIndices = nullptr;
*/
// Row Indices, Column Indices, Values, Choice Indices
size_t const matrixDataSize = ((rowCount + 1) * indexTypeSize) + (nnzCount * indexTypeSize) + (nnzCount * valueTypeSize) + ((rowGroupCount + 1) * indexTypeSize);
// Vectors x, xSwap, b, multiplyResult
size_t const vectorSizes = (rowGroupCount * valueTypeSize) + (rowGroupCount * valueTypeSize) + (rowCount * valueTypeSize) + (rowCount * valueTypeSize);
return (matrixDataSize + vectorSizes);
}
size_t basicValueIteration_mvReduce_uint64_float_calculateMemorySize(size_t const rowCount, size_t const rowGroupCount, size_t const nnzCount) {
size_t const valueTypeSize = sizeof(float);
size_t const indexTypeSize = sizeof(uint_fast64_t);
/*
IndexType* device_matrixRowIndices = nullptr;
IndexType* device_matrixColIndices = nullptr;
ValueType* device_matrixValues = nullptr;
ValueType* device_x = nullptr;
ValueType* device_xSwap = nullptr;
ValueType* device_b = nullptr;
ValueType* device_multiplyResult = nullptr;
IndexType* device_nondeterministicChoiceIndices = nullptr;
*/
// Row Indices, Column Indices, Values, Choice Indices
size_t const matrixDataSize = ((rowCount + 1) * indexTypeSize) + (nnzCount * indexTypeSize) + (nnzCount * valueTypeSize) + ((rowGroupCount + 1) * indexTypeSize);
// Vectors x, xSwap, b, multiplyResult
size_t const vectorSizes = (rowGroupCount * valueTypeSize) + (rowGroupCount * valueTypeSize) + (rowCount * valueTypeSize) + (rowCount * valueTypeSize);
return (matrixDataSize + vectorSizes);
}

107
resources/cudaForStorm/srcCuda/basicValueIteration.h
View File

@ -1,107 +0,0 @@
#ifndef STORM_CUDAFORSTORM_BASICVALUEITERATION_H_
#define STORM_CUDAFORSTORM_BASICVALUEITERATION_H_
#include <cstdint>
#include <vector>
#include <utility>
// Library exports
#include "cudaForStorm_Export.h"
/* Helper declaration to cope with new internal format */
#ifndef STORM_STORAGE_SPARSEMATRIX_H_
namespace storm {
namespace storage {
template<typename T>
class MatrixEntry {
public:
/*!
* Constructs a matrix entry with the given column and value.
*
* @param column The column of the matrix entry.
* @param value The value of the matrix entry.
*/
MatrixEntry(uint_fast64_t column, T value);
/*!
* Move-constructs the matrix entry fro the given column-value pair.
*
* @param pair The column-value pair from which to move-construct the matrix entry.
*/
MatrixEntry(std::pair<uint_fast64_t, T>&& pair);
//MatrixEntry() = default;
//MatrixEntry(MatrixEntry const& other) = default;
//MatrixEntry& operator=(MatrixEntry const& other) = default;
#ifndef WINDOWS
//MatrixEntry(MatrixEntry&& other) = default;
//MatrixEntry& operator=(MatrixEntry&& other) = default;
#endif
/*!
* Retrieves the column of the matrix entry.
*
* @return The column of the matrix entry.
*/
uint_fast64_t const& getColumn() const;
/*!
* Retrieves the column of the matrix entry.
*
* @return The column of the matrix entry.
*/
uint_fast64_t& getColumn();
/*!
* Retrieves the value of the matrix entry.
*
* @return The value of the matrix entry.
*/
T const& getValue() const;
/*!
* Retrieves the value of the matrix entry.
*
* @return The value of the matrix entry.
*/
T& getValue();
/*!
* Retrieves a pair of column and value that characterizes this entry.
*
* @return A column-value pair that characterizes this entry.
*/
std::pair<uint_fast64_t, T> const& getColumnValuePair() const;
private:
// The actual matrix entry.
std::pair<uint_fast64_t, T> entry;
};
}
}
#endif
cudaForStorm_EXPORT size_t basicValueIteration_mvReduce_uint64_double_calculateMemorySize(size_t const rowCount, size_t const rowGroupCount, size_t const nnzCount);
cudaForStorm_EXPORT bool basicValueIteration_mvReduce_uint64_double_minimize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<double>> const& columnIndicesAndValues, std::vector<double>& x, std::vector<double> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount);
cudaForStorm_EXPORT bool basicValueIteration_mvReduce_uint64_double_maximize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<double>> const& columnIndicesAndValues, std::vector<double>& x, std::vector<double> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount);
cudaForStorm_EXPORT size_t basicValueIteration_mvReduce_uint64_float_calculateMemorySize(size_t const rowCount, size_t const rowGroupCount, size_t const nnzCount);
cudaForStorm_EXPORT bool basicValueIteration_mvReduce_uint64_float_minimize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<float>> const& columnIndicesAndValues, std::vector<float>& x, std::vector<float> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount);
cudaForStorm_EXPORT bool basicValueIteration_mvReduce_uint64_float_maximize(uint_fast64_t const maxIterationCount, double const precision, bool const relativePrecisionCheck, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<float>> const& columnIndicesAndValues, std::vector<float>& x, std::vector<float> const& b, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, size_t& iterationCount);
cudaForStorm_EXPORT void basicValueIteration_spmv_uint64_double(uint_fast64_t const matrixColCount, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<double>> const& columnIndicesAndValues, std::vector<double> const& x, std::vector<double>& b);
cudaForStorm_EXPORT void basicValueIteration_addVectorsInplace_double(std::vector<double>& a, std::vector<double> const& b);
cudaForStorm_EXPORT void basicValueIteration_reduceGroupedVector_uint64_double_minimize(std::vector<double> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<double>& targetVector);
cudaForStorm_EXPORT void basicValueIteration_reduceGroupedVector_uint64_double_maximize(std::vector<double> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<double>& targetVector);
cudaForStorm_EXPORT void basicValueIteration_equalModuloPrecision_double_Relative(std::vector<double> const& x, std::vector<double> const& y, double& maxElement);
cudaForStorm_EXPORT void basicValueIteration_equalModuloPrecision_double_NonRelative(std::vector<double> const& x, std::vector<double> const& y, double& maxElement);
cudaForStorm_EXPORT void basicValueIteration_spmv_uint64_float(uint_fast64_t const matrixColCount, std::vector<uint_fast64_t> const& matrixRowIndices, std::vector<storm::storage::MatrixEntry<float>> const& columnIndicesAndValues, std::vector<float> const& x, std::vector<float>& b);
cudaForStorm_EXPORT void basicValueIteration_addVectorsInplace_float(std::vector<float>& a, std::vector<float> const& b);
cudaForStorm_EXPORT void basicValueIteration_reduceGroupedVector_uint64_float_minimize(std::vector<float> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<float>& targetVector);
cudaForStorm_EXPORT void basicValueIteration_reduceGroupedVector_uint64_float_maximize(std::vector<float> const& groupedVector, std::vector<uint_fast64_t> const& grouping, std::vector<float>& targetVector);
cudaForStorm_EXPORT void basicValueIteration_equalModuloPrecision_float_Relative(std::vector<float> const& x, std::vector<float> const& y, float& maxElement);
cudaForStorm_EXPORT void basicValueIteration_equalModuloPrecision_float_NonRelative(std::vector<float> const& x, std::vector<float> const& y, float& maxElement);
#endif // STORM_CUDAFORSTORM_BASICVALUEITERATION_H_

19
resources/cudaForStorm/srcCuda/cudaForStorm.h
View File

@ -1,19 +0,0 @@
#ifndef STORM_CUDAFORSTORM_CUDAFORSTORM_H_
#define STORM_CUDAFORSTORM_CUDAFORSTORM_H_
/*
* List of exported functions in this library
*/
// TopologicalValueIteration
#include "srcCuda/basicValueIteration.h"
// Utility Functions
#include "srcCuda/utility.h"
// Version Information
#include "srcCuda/version.h"
#endif // STORM_CUDAFORSTORM_CUDAFORSTORM_H_

49
resources/cudaForStorm/srcCuda/cuspExtension.h
View File

@ -1,49 +0,0 @@
#pragma once
#include "cuspExtensionFloat.h"
#include "cuspExtensionDouble.h"
namespace cusp {
namespace detail {
namespace device {
template <typename ValueType>
void storm_cuda_opt_spmv_csr_vector(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const ValueType * matrixColumnIndicesAndValues, const ValueType* x, ValueType* y) {
//
throw;
}
template <>
void storm_cuda_opt_spmv_csr_vector<double>(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const double * matrixColumnIndicesAndValues, const double* x, double* y) {
storm_cuda_opt_spmv_csr_vector_double(num_rows, num_entries, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
}
template <>
void storm_cuda_opt_spmv_csr_vector<float>(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const float * matrixColumnIndicesAndValues, const float* x, float* y) {
storm_cuda_opt_spmv_csr_vector_float(num_rows, num_entries, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
}
template <bool Minimize, typename ValueType>
void storm_cuda_opt_vector_reduce(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, ValueType * x, const ValueType * y) {
//
throw;
}
template <>
void storm_cuda_opt_vector_reduce<true, double>(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, double * x, const double * y) {
storm_cuda_opt_vector_reduce_double<true>(num_rows, num_entries, nondeterministicChoiceIndices, x, y);
}
template <>
void storm_cuda_opt_vector_reduce<false, double>(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, double * x, const double * y) {
storm_cuda_opt_vector_reduce_double<false>(num_rows, num_entries, nondeterministicChoiceIndices, x, y);
}
template <>
void storm_cuda_opt_vector_reduce<true, float>(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, float * x, const float * y) {
storm_cuda_opt_vector_reduce_float<true>(num_rows, num_entries, nondeterministicChoiceIndices, x, y);
}
template <>
void storm_cuda_opt_vector_reduce<false, float>(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, float * x, const float * y) {
storm_cuda_opt_vector_reduce_float<false>(num_rows, num_entries, nondeterministicChoiceIndices, x, y);
}
} // end namespace device
} // end namespace detail
} // end namespace cusp

361
resources/cudaForStorm/srcCuda/cuspExtensionDouble.h
View File

@ -1,361 +0,0 @@
/*
* This is an extension of the original CUSP csr_vector.h SPMV implementation.
* It is based on the Code and incorporates changes as to cope with the details
* of the StoRM code.
* Changes have been made for 1) different input format, 2) the sum calculation and 3) the group-reduce algorithm
*/
/*
* Copyright 2008-2009 NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <limits>
#include <cstdint>
#include <algorithm>
#include <math_functions.h>
#include <cusp/detail/device/spmv/csr_vector.h>
namespace cusp
{
namespace detail
{
namespace device
{
//////////////////////////////////////////////////////////////////////////////
// CSR SpMV kernels based on a vector model (one warp per row)
//////////////////////////////////////////////////////////////////////////////
//
// spmv_csr_vector_device
// Each row of the CSR matrix is assigned to a warp. The warp computes
// y[i] = A[i,:] * x, i.e. the dot product of the i-th row of A with
// the x vector, in parallel. This division of work implies that
// the CSR index and data arrays (Aj and Ax) are accessed in a contiguous
// manner (but generally not aligned). On GT200 these accesses are
// coalesced, unlike kernels based on the one-row-per-thread division of
// work. Since an entire 32-thread warp is assigned to each row, many
// threads will remain idle when their row contains a small number
// of elements. This code relies on implicit synchronization among
// threads in a warp.
//
// spmv_csr_vector_tex_device
// Same as spmv_csr_vector_tex_device, except that the texture cache is
// used for accessing the x vector.
//
// Note: THREADS_PER_VECTOR must be one of [2,4,8,16,32]
template <unsigned int VECTORS_PER_BLOCK, unsigned int THREADS_PER_VECTOR, bool UseCache>
__launch_bounds__(VECTORS_PER_BLOCK * THREADS_PER_VECTOR,1)
__global__ void
storm_cuda_opt_spmv_csr_vector_kernel_double(const uint_fast64_t num_rows, const uint_fast64_t * __restrict__ matrixRowIndices, const double * __restrict__ matrixColumnIndicesAndValues, const double * __restrict__ x, double * __restrict__ y)
{
__shared__ volatile double sdata[VECTORS_PER_BLOCK * THREADS_PER_VECTOR + THREADS_PER_VECTOR / 2]; // padded to avoid reduction conditionals
__shared__ volatile uint_fast64_t ptrs[VECTORS_PER_BLOCK][2];
const uint_fast64_t THREADS_PER_BLOCK = VECTORS_PER_BLOCK * THREADS_PER_VECTOR;
const uint_fast64_t thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index
const uint_fast64_t thread_lane = threadIdx.x & (THREADS_PER_VECTOR - 1); // thread index within the vector
const uint_fast64_t vector_id = thread_id / THREADS_PER_VECTOR; // global vector index
const uint_fast64_t vector_lane = threadIdx.x / THREADS_PER_VECTOR; // vector index within the block
const uint_fast64_t num_vectors = VECTORS_PER_BLOCK * gridDim.x; // total number of active vectors
for(uint_fast64_t row = vector_id; row < num_rows; row += num_vectors)
{
// use two threads to fetch Ap[row] and Ap[row+1]
// this is considerably faster than the straightforward version
if(thread_lane < 2)
ptrs[vector_lane][thread_lane] = matrixRowIndices[row + thread_lane];
const uint_fast64_t row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row];
const uint_fast64_t row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1];
// initialize local sum
double sum = 0;
if (THREADS_PER_VECTOR == 32 && row_end - row_start > 32)
{
// ensure aligned memory access to Aj and Ax
uint_fast64_t jj = row_start - (row_start & (THREADS_PER_VECTOR - 1)) + thread_lane;
// accumulate local sums
if(jj >= row_start && jj < row_end) {
sum += matrixColumnIndicesAndValues[2 * jj + 1] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 2 * jj), x);
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
}
// accumulate local sums
for(jj += THREADS_PER_VECTOR; jj < row_end; jj += THREADS_PER_VECTOR) {
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
sum += matrixColumnIndicesAndValues[2 * jj + 1] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 2 * jj), x);
}
} else {
// accumulate local sums
for(uint_fast64_t jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_VECTOR) {
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
sum += matrixColumnIndicesAndValues[2 * jj + 1] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 2 * jj), x);
}
}
// store local sum in shared memory
sdata[threadIdx.x] = sum;
// reduce local sums to row sum
if (THREADS_PER_VECTOR > 16) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 16];
if (THREADS_PER_VECTOR > 8) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 8];
if (THREADS_PER_VECTOR > 4) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 4];
if (THREADS_PER_VECTOR > 2) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 2];
if (THREADS_PER_VECTOR > 1) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 1];
// first thread writes the result
if (thread_lane == 0)
y[row] = sdata[threadIdx.x];
}
}
template <unsigned int ROWS_PER_BLOCK, unsigned int THREADS_PER_ROW, bool Minimize>
__launch_bounds__(ROWS_PER_BLOCK * THREADS_PER_ROW,1)
__global__ void
storm_cuda_opt_vector_reduce_kernel_double(const uint_fast64_t num_rows, const uint_fast64_t * __restrict__ nondeterministicChoiceIndices, double * __restrict__ x, const double * __restrict__ y, const double minMaxInitializer)
{
__shared__ volatile double sdata[ROWS_PER_BLOCK * THREADS_PER_ROW + THREADS_PER_ROW / 2]; // padded to avoid reduction conditionals
__shared__ volatile uint_fast64_t ptrs[ROWS_PER_BLOCK][2];
const uint_fast64_t THREADS_PER_BLOCK = ROWS_PER_BLOCK * THREADS_PER_ROW;
const uint_fast64_t thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index
const uint_fast64_t thread_lane = threadIdx.x & (THREADS_PER_ROW - 1); // thread index within the vector
const uint_fast64_t vector_id = thread_id / THREADS_PER_ROW; // global vector index
const uint_fast64_t vector_lane = threadIdx.x / THREADS_PER_ROW; // vector index within the block
const uint_fast64_t num_vectors = ROWS_PER_BLOCK * gridDim.x; // total number of active vectors
for(uint_fast64_t row = vector_id; row < num_rows; row += num_vectors)
{
// use two threads to fetch Ap[row] and Ap[row+1]
// this is considerably faster than the straightforward version
if(thread_lane < 2)
ptrs[vector_lane][thread_lane] = nondeterministicChoiceIndices[row + thread_lane];
const uint_fast64_t row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row];
const uint_fast64_t row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1];
// initialize local Min/Max
double localMinMaxElement = minMaxInitializer;
if (THREADS_PER_ROW == 32 && row_end - row_start > 32)
{
// ensure aligned memory access to Aj and Ax
uint_fast64_t jj = row_start - (row_start & (THREADS_PER_ROW - 1)) + thread_lane;
// accumulate local sums
if(jj >= row_start && jj < row_end) {
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
// accumulate local sums
for(jj += THREADS_PER_ROW; jj < row_end; jj += THREADS_PER_ROW)
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
else
{
// accumulate local sums
for(uint_fast64_t jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_ROW)
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
// store local sum in shared memory
sdata[threadIdx.x] = localMinMaxElement;
// reduce local min/max to row min/max
if (Minimize) {
/*if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement);*/
if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 16]);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 8]);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 4]);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 2]);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 1]);
} else {
/*if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement);*/
if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 16]);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 8]);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 4]);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 2]);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 1]);
}
// first thread writes the result
if (thread_lane == 0)
x[row] = sdata[threadIdx.x];
}
}
template <bool Minimize, unsigned int THREADS_PER_VECTOR>
void __storm_cuda_opt_vector_reduce_double(const uint_fast64_t num_rows, const uint_fast64_t * nondeterministicChoiceIndices, double * x, const double * y)
{
double __minMaxInitializer = -std::numeric_limits<double>::max();
if (Minimize) {
__minMaxInitializer = std::numeric_limits<double>::max();
}
const double minMaxInitializer = __minMaxInitializer;
const size_t THREADS_PER_BLOCK = 128;
const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR;
const size_t MAX_BLOCKS = cusp::detail::device::arch::max_active_blocks(storm_cuda_opt_vector_reduce_kernel_double<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, Minimize>, THREADS_PER_BLOCK, (size_t) 0);
const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK));
storm_cuda_opt_vector_reduce_kernel_double<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, Minimize> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>>
(num_rows, nondeterministicChoiceIndices, x, y, minMaxInitializer);
}
template <bool Minimize>
void storm_cuda_opt_vector_reduce_double(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, double * x, const double * y)
{
const uint_fast64_t rows_per_group = num_entries / num_rows;
if (rows_per_group <= 2) { __storm_cuda_opt_vector_reduce_double<Minimize, 2>(num_rows, nondeterministicChoiceIndices, x, y); return; }
if (rows_per_group <= 4) { __storm_cuda_opt_vector_reduce_double<Minimize, 4>(num_rows, nondeterministicChoiceIndices, x, y); return; }
if (rows_per_group <= 8) { __storm_cuda_opt_vector_reduce_double<Minimize, 8>(num_rows, nondeterministicChoiceIndices, x, y); return; }
if (rows_per_group <= 16) { __storm_cuda_opt_vector_reduce_double<Minimize,16>(num_rows, nondeterministicChoiceIndices, x, y); return; }
__storm_cuda_opt_vector_reduce_double<Minimize,32>(num_rows, nondeterministicChoiceIndices, x, y);
}
template <bool UseCache, unsigned int THREADS_PER_VECTOR>
void __storm_cuda_opt_spmv_csr_vector_double(const uint_fast64_t num_rows, const uint_fast64_t * matrixRowIndices, const double * matrixColumnIndicesAndValues, const double* x, double* y)
{
const size_t THREADS_PER_BLOCK = 128;
const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR;
const size_t MAX_BLOCKS = cusp::detail::device::arch::max_active_blocks(storm_cuda_opt_spmv_csr_vector_kernel_double<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache>, THREADS_PER_BLOCK, (size_t) 0);
const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK));
if (UseCache)
bind_x(x);
storm_cuda_opt_spmv_csr_vector_kernel_double<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>>
(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
if (UseCache)
unbind_x(x);
}
void storm_cuda_opt_spmv_csr_vector_double(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const double * matrixColumnIndicesAndValues, const double* x, double* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector_double<false, 2>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector_double<false, 4>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector_double<false, 8>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector_double<false,16>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
__storm_cuda_opt_spmv_csr_vector_double<false,32>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
}
void storm_cuda_opt_spmv_csr_vector_tex(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const double * matrixColumnIndicesAndValues, const double* x, double* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector_double<true, 2>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector_double<true, 4>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector_double<true, 8>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector_double<true,16>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
__storm_cuda_opt_spmv_csr_vector_double<true,32>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
}
// NON-OPT
template <bool UseCache, unsigned int THREADS_PER_VECTOR>
void __storm_cuda_spmv_csr_vector_double(const uint_fast64_t num_rows, const uint_fast64_t * matrixRowIndices, const uint_fast64_t * matrixColumnIndices, const double * matrixValues, const double* x, double* y)
{
const size_t THREADS_PER_BLOCK = 128;
const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR;
const size_t MAX_BLOCKS = cusp::detail::device::arch::max_active_blocks(spmv_csr_vector_kernel<uint_fast64_t, double, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache>, THREADS_PER_BLOCK, (size_t) 0);
const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK));
if (UseCache)
bind_x(x);
spmv_csr_vector_kernel<uint_fast64_t, double, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>>
(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y);
if (UseCache)
unbind_x(x);
}
void storm_cuda_spmv_csr_vector_double(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const uint_fast64_t * matrixColumnIndices, const double * matrixValues, const double* x, double* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_spmv_csr_vector_double<false, 2>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_spmv_csr_vector_double<false, 4>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_spmv_csr_vector_double<false, 8>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_spmv_csr_vector_double<false,16>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
__storm_cuda_spmv_csr_vector_double<false,32>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y);
}
void storm_cuda_spmv_csr_vector_tex_double(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const uint_fast64_t * matrixColumnIndices, const double * matrixValues, const double* x, double* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_spmv_csr_vector_double<true, 2>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_spmv_csr_vector_double<true, 4>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_spmv_csr_vector_double<true, 8>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_spmv_csr_vector_double<true,16>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
__storm_cuda_spmv_csr_vector_double<true,32>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y);
}
} // end namespace device
} // end namespace detail
} // end namespace cusp

375
resources/cudaForStorm/srcCuda/cuspExtensionFloat.h
View File

@ -1,375 +0,0 @@
/*
* This is an extension of the original CUSP csr_vector.h SPMV implementation.
* It is based on the Code and incorporates changes as to cope with the details
* of the StoRM code.
* As this is mostly copy & paste, the original license still applies.
*/
/*
* Copyright 2008-2009 NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <limits>
#include <cstdint>
#include <algorithm>
#include <math_functions.h>
#include <cusp/detail/device/spmv/csr_vector.h>
#include "storm-cudaplugin-config.h"
namespace cusp
{
namespace detail
{
namespace device
{
//////////////////////////////////////////////////////////////////////////////
// CSR SpMV kernels based on a vector model (one warp per row)
//////////////////////////////////////////////////////////////////////////////
//
// spmv_csr_vector_device
// Each row of the CSR matrix is assigned to a warp. The warp computes
// y[i] = A[i,:] * x, i.e. the dot product of the i-th row of A with
// the x vector, in parallel. This division of work implies that
// the CSR index and data arrays (Aj and Ax) are accessed in a contiguous
// manner (but generally not aligned). On GT200 these accesses are
// coalesced, unlike kernels based on the one-row-per-thread division of
// work. Since an entire 32-thread warp is assigned to each row, many
// threads will remain idle when their row contains a small number
// of elements. This code relies on implicit synchronization among
// threads in a warp.
//
// spmv_csr_vector_tex_device
// Same as spmv_csr_vector_tex_device, except that the texture cache is
// used for accessing the x vector.
//
// Note: THREADS_PER_VECTOR must be one of [2,4,8,16,32]
template <unsigned int VECTORS_PER_BLOCK, unsigned int THREADS_PER_VECTOR, bool UseCache>
__launch_bounds__(VECTORS_PER_BLOCK * THREADS_PER_VECTOR,1)
__global__ void
storm_cuda_opt_spmv_csr_vector_kernel_float(const uint_fast64_t num_rows, const uint_fast64_t * matrixRowIndices, const float * matrixColumnIndicesAndValues, const float * x, float * y)
{
__shared__ volatile float sdata[VECTORS_PER_BLOCK * THREADS_PER_VECTOR + THREADS_PER_VECTOR / 2]; // padded to avoid reduction conditionals
__shared__ volatile uint_fast64_t ptrs[VECTORS_PER_BLOCK][2];
const uint_fast64_t THREADS_PER_BLOCK = VECTORS_PER_BLOCK * THREADS_PER_VECTOR;
const uint_fast64_t thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index
const uint_fast64_t thread_lane = threadIdx.x & (THREADS_PER_VECTOR - 1); // thread index within the vector
const uint_fast64_t vector_id = thread_id / THREADS_PER_VECTOR; // global vector index
const uint_fast64_t vector_lane = threadIdx.x / THREADS_PER_VECTOR; // vector index within the block
const uint_fast64_t num_vectors = VECTORS_PER_BLOCK * gridDim.x; // total number of active vectors
for(uint_fast64_t row = vector_id; row < num_rows; row += num_vectors)
{
// use two threads to fetch Ap[row] and Ap[row+1]
// this is considerably faster than the straightforward version
if(thread_lane < 2)
ptrs[vector_lane][thread_lane] = matrixRowIndices[row + thread_lane];
const uint_fast64_t row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row];
const uint_fast64_t row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1];
// initialize local sum
float sum = 0;
if (THREADS_PER_VECTOR == 32 && row_end - row_start > 32)
{
// ensure aligned memory access to Aj and Ax
uint_fast64_t jj = row_start - (row_start & (THREADS_PER_VECTOR - 1)) + thread_lane;
// accumulate local sums
if(jj >= row_start && jj < row_end) {
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
sum += matrixColumnIndicesAndValues[4 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 4 * jj), x);
#else
sum += matrixColumnIndicesAndValues[3 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 3 * jj), x);
#endif
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
}
// accumulate local sums
for(jj += THREADS_PER_VECTOR; jj < row_end; jj += THREADS_PER_VECTOR) {
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
sum += matrixColumnIndicesAndValues[4 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 4 * jj), x);
#else
sum += matrixColumnIndicesAndValues[3 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 3 * jj), x);
#endif
}
} else {
// accumulate local sums
for(uint_fast64_t jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_VECTOR) {
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
sum += matrixColumnIndicesAndValues[4 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 4 * jj), x);
#else
sum += matrixColumnIndicesAndValues[3 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 3 * jj), x);
#endif
}
}
// store local sum in shared memory
sdata[threadIdx.x] = sum;
// reduce local sums to row sum
if (THREADS_PER_VECTOR > 16) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 16];
if (THREADS_PER_VECTOR > 8) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 8];
if (THREADS_PER_VECTOR > 4) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 4];
if (THREADS_PER_VECTOR > 2) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 2];
if (THREADS_PER_VECTOR > 1) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 1];
// first thread writes the result
if (thread_lane == 0)
y[row] = sdata[threadIdx.x];
}
}
template <unsigned int ROWS_PER_BLOCK, unsigned int THREADS_PER_ROW, bool Minimize>
__launch_bounds__(ROWS_PER_BLOCK * THREADS_PER_ROW,1)
__global__ void
storm_cuda_opt_vector_reduce_kernel_float(const uint_fast64_t num_rows, const uint_fast64_t * nondeterministicChoiceIndices, float * x, const float * y, const float minMaxInitializer)
{
__shared__ volatile float sdata[ROWS_PER_BLOCK * THREADS_PER_ROW + THREADS_PER_ROW / 2]; // padded to avoid reduction conditionals
__shared__ volatile uint_fast64_t ptrs[ROWS_PER_BLOCK][2];
const uint_fast64_t THREADS_PER_BLOCK = ROWS_PER_BLOCK * THREADS_PER_ROW;
const uint_fast64_t thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index
const uint_fast64_t thread_lane = threadIdx.x & (THREADS_PER_ROW - 1); // thread index within the vector
const uint_fast64_t vector_id = thread_id / THREADS_PER_ROW; // global vector index
const uint_fast64_t vector_lane = threadIdx.x / THREADS_PER_ROW; // vector index within the block
const uint_fast64_t num_vectors = ROWS_PER_BLOCK * gridDim.x; // total number of active vectors
for(uint_fast64_t row = vector_id; row < num_rows; row += num_vectors)
{
// use two threads to fetch Ap[row] and Ap[row+1]
// this is considerably faster than the straightforward version
if(thread_lane < 2)
ptrs[vector_lane][thread_lane] = nondeterministicChoiceIndices[row + thread_lane];
const uint_fast64_t row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row];
const uint_fast64_t row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1];
// initialize local Min/Max
float localMinMaxElement = minMaxInitializer;
if (THREADS_PER_ROW == 32 && row_end - row_start > 32)
{
// ensure aligned memory access to Aj and Ax
uint_fast64_t jj = row_start - (row_start & (THREADS_PER_ROW - 1)) + thread_lane;
// accumulate local sums
if(jj >= row_start && jj < row_end) {
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
// accumulate local sums
for(jj += THREADS_PER_ROW; jj < row_end; jj += THREADS_PER_ROW)
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
else
{
// accumulate local sums
for(uint_fast64_t jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_ROW)
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
// store local sum in shared memory
sdata[threadIdx.x] = localMinMaxElement;
// reduce local min/max to row min/max
if (Minimize) {
/*if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement);*/
if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 16]);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 8]);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 4]);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 2]);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 1]);
} else {
/*if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement);*/
if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 16]);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 8]);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 4]);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 2]);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 1]);
}
// first thread writes the result
if (thread_lane == 0)
x[row] = sdata[threadIdx.x];
}
}
template <bool Minimize, unsigned int THREADS_PER_VECTOR>
void __storm_cuda_opt_vector_reduce_float(const uint_fast64_t num_rows, const uint_fast64_t * nondeterministicChoiceIndices, float * x, const float * y)
{
float __minMaxInitializer = -std::numeric_limits<float>::max();
if (Minimize) {
__minMaxInitializer = std::numeric_limits<float>::max();
}
const float minMaxInitializer = __minMaxInitializer;
const size_t THREADS_PER_BLOCK = 128;
const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR;
const size_t MAX_BLOCKS = cusp::detail::device::arch::max_active_blocks(storm_cuda_opt_vector_reduce_kernel_float<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, Minimize>, THREADS_PER_BLOCK, (size_t) 0);
const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK));
storm_cuda_opt_vector_reduce_kernel_float<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, Minimize> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>>
(num_rows, nondeterministicChoiceIndices, x, y, minMaxInitializer);
}
template <bool Minimize>
void storm_cuda_opt_vector_reduce_float(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * nondeterministicChoiceIndices, float * x, const float * y)
{
const uint_fast64_t rows_per_group = num_entries / num_rows;
if (rows_per_group <= 2) { __storm_cuda_opt_vector_reduce_float<Minimize, 2>(num_rows, nondeterministicChoiceIndices, x, y); return; }
if (rows_per_group <= 4) { __storm_cuda_opt_vector_reduce_float<Minimize, 4>(num_rows, nondeterministicChoiceIndices, x, y); return; }
if (rows_per_group <= 8) { __storm_cuda_opt_vector_reduce_float<Minimize, 8>(num_rows, nondeterministicChoiceIndices, x, y); return; }
if (rows_per_group <= 16) { __storm_cuda_opt_vector_reduce_float<Minimize,16>(num_rows, nondeterministicChoiceIndices, x, y); return; }
__storm_cuda_opt_vector_reduce_float<Minimize,32>(num_rows, nondeterministicChoiceIndices, x, y);
}
template <bool UseCache, unsigned int THREADS_PER_VECTOR>
void __storm_cuda_opt_spmv_csr_vector_float(const uint_fast64_t num_rows, const uint_fast64_t * matrixRowIndices, const float * matrixColumnIndicesAndValues, const float* x, float* y)
{
const size_t THREADS_PER_BLOCK = 128;
const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR;
const size_t MAX_BLOCKS = cusp::detail::device::arch::max_active_blocks(storm_cuda_opt_spmv_csr_vector_kernel_float<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache>, THREADS_PER_BLOCK, (size_t) 0);
const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK));
if (UseCache)
bind_x(x);
storm_cuda_opt_spmv_csr_vector_kernel_float<VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>>
(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
if (UseCache)
unbind_x(x);
}
void storm_cuda_opt_spmv_csr_vector_float(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const float * matrixColumnIndicesAndValues, const float* x, float* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector_float<false, 2>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector_float<false, 4>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector_float<false, 8>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector_float<false,16>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
__storm_cuda_opt_spmv_csr_vector_float<false,32>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
}
void storm_cuda_opt_spmv_csr_vector_tex(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const float * matrixColumnIndicesAndValues, const float* x, float* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector_float<true, 2>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector_float<true, 4>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector_float<true, 8>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector_float<true,16>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; }
__storm_cuda_opt_spmv_csr_vector_float<true,32>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y);
}
// NON-OPT
template <bool UseCache, unsigned int THREADS_PER_VECTOR>
void __storm_cuda_spmv_csr_vector_float(const uint_fast64_t num_rows, const uint_fast64_t * matrixRowIndices, const uint_fast64_t * matrixColumnIndices, const float * matrixValues, const float* x, float* y)
{
const size_t THREADS_PER_BLOCK = 128;
const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR;
const size_t MAX_BLOCKS = cusp::detail::device::arch::max_active_blocks(spmv_csr_vector_kernel<uint_fast64_t, float, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache>, THREADS_PER_BLOCK, (size_t) 0);
const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK));
if (UseCache)
bind_x(x);
spmv_csr_vector_kernel<uint_fast64_t, float, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>>
(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y);
if (UseCache)
unbind_x(x);
}
void storm_cuda_spmv_csr_vector_float(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const uint_fast64_t * matrixColumnIndices, const float * matrixValues, const float* x, float* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_spmv_csr_vector_float<false, 2>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_spmv_csr_vector_float<false, 4>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_spmv_csr_vector_float<false, 8>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_spmv_csr_vector_float<false,16>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
__storm_cuda_spmv_csr_vector_float<false,32>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y);
}
void storm_cuda_spmv_csr_vector_tex_float(const uint_fast64_t num_rows, const uint_fast64_t num_entries, const uint_fast64_t * matrixRowIndices, const uint_fast64_t * matrixColumnIndices, const float * matrixValues, const float* x, float* y)
{
const uint_fast64_t nnz_per_row = num_entries / num_rows;
if (nnz_per_row <= 2) { __storm_cuda_spmv_csr_vector_float<true, 2>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 4) { __storm_cuda_spmv_csr_vector_float<true, 4>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 8) { __storm_cuda_spmv_csr_vector_float<true, 8>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
if (nnz_per_row <= 16) { __storm_cuda_spmv_csr_vector_float<true,16>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; }
__storm_cuda_spmv_csr_vector_float<true,32>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y);
}
} // end namespace device
} // end namespace detail
} // end namespace cusp

39
resources/cudaForStorm/srcCuda/kernelSwitchTest.cu
View File

@ -1,39 +0,0 @@
#include <iostream>
#include <chrono>
__global__ void cuda_kernel_kernelSwitchTest(int const * const A, int * const B) {
*B = *A;
}
void kernelSwitchTest(size_t N) {
int* deviceIntA;
int* deviceIntB;
if (cudaMalloc((void**)&deviceIntA, sizeof(int)) != cudaSuccess) {
std::cout << "Error in cudaMalloc while allocating " << sizeof(int) << " Bytes!" << std::endl;
return;
}
if (cudaMalloc((void**)&deviceIntB, sizeof(int)) != cudaSuccess) {
std::cout << "Error in cudaMalloc while allocating " << sizeof(int) << " Bytes!" << std::endl;
return;
}
// Allocate space on the device
auto start_time = std::chrono::high_resolution_clock::now();
for (int i = 0; i < N; ++i) {
cuda_kernel_kernelSwitchTest<<<1,1>>>(deviceIntA, deviceIntB);
}
auto end_time = std::chrono::high_resolution_clock::now();
std::cout << "Switching the Kernel " << N << " times took " << std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count() << "micros" << std::endl;
std::cout << "Resulting in " << (std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count() / ((double)(N))) << "Microseconds per Kernel Switch" << std::endl;
// Free memory on device
if (cudaFree(deviceIntA) != cudaSuccess) {
std::cout << "Error in cudaFree!" << std::endl;
return;
}
if (cudaFree(deviceIntB) != cudaSuccess) {
std::cout << "Error in cudaFree!" << std::endl;
return;
}
}

1
resources/cudaForStorm/srcCuda/kernelSwitchTest.h
View File

@ -1 +0,0 @@
void kernelSwitchTest(size_t N);

33
resources/cudaForStorm/srcCuda/utility.cu
View File

@ -1,33 +0,0 @@
#include "utility.h"
#include <cuda_runtime.h>
size_t getFreeCudaMemory() {
size_t freeMemory;
size_t totalMemory;
cudaMemGetInfo(&freeMemory, &totalMemory);
return freeMemory;
}
size_t getTotalCudaMemory() {
size_t freeMemory;
size_t totalMemory;
cudaMemGetInfo(&freeMemory, &totalMemory);
return totalMemory;
}
bool resetCudaDevice() {
cudaError_t result = cudaDeviceReset();
return (result == cudaSuccess);
}
int getRuntimeCudaVersion() {
int result = -1;
cudaError_t errorResult = cudaRuntimeGetVersion(&result);
if (errorResult != cudaSuccess) {
return -1;
}
return result;
}

12
resources/cudaForStorm/srcCuda/utility.h
View File

@ -1,12 +0,0 @@
#ifndef STORM_CUDAFORSTORM_UTILITY_H_
#define STORM_CUDAFORSTORM_UTILITY_H_
// Library exports
#include "cudaForStorm_Export.h"
cudaForStorm_EXPORT size_t getFreeCudaMemory();
cudaForStorm_EXPORT size_t getTotalCudaMemory();
cudaForStorm_EXPORT bool resetCudaDevice();
cudaForStorm_EXPORT int getRuntimeCudaVersion();
#endif // STORM_CUDAFORSTORM_UTILITY_H_

28
resources/cudaForStorm/srcCuda/version.cu
View File

@ -1,28 +0,0 @@
#include "version.h"
#include "storm-cudaplugin-config.h"
size_t getStormCudaPluginVersionMajor() {
return STORM_CUDAPLUGIN_VERSION_MAJOR;
}
size_t getStormCudaPluginVersionMinor() {
return STORM_CUDAPLUGIN_VERSION_MINOR;
}
size_t getStormCudaPluginVersionPatch() {
return STORM_CUDAPLUGIN_VERSION_PATCH;
}
size_t getStormCudaPluginVersionCommitsAhead() {
return STORM_CUDAPLUGIN_VERSION_COMMITS_AHEAD;
}
const char* getStormCudaPluginVersionHash() {
static const std::string versionHash = STORM_CUDAPLUGIN_VERSION_HASH;
return versionHash.c_str();
}
bool getStormCudaPluginVersionIsDirty() {
return ((STORM_CUDAPLUGIN_VERSION_DIRTY) != 0);
}

16
resources/cudaForStorm/srcCuda/version.h
View File

@ -1,16 +0,0 @@
#ifndef STORM_CUDAFORSTORM_VERSION_H_
#define STORM_CUDAFORSTORM_VERSION_H_
// Library exports
#include "cudaForStorm_Export.h"
#include <string>
cudaForStorm_EXPORT size_t getStormCudaPluginVersionMajor();
cudaForStorm_EXPORT size_t getStormCudaPluginVersionMinor();
cudaForStorm_EXPORT size_t getStormCudaPluginVersionPatch();
cudaForStorm_EXPORT size_t getStormCudaPluginVersionCommitsAhead();
cudaForStorm_EXPORT const char* getStormCudaPluginVersionHash();
cudaForStorm_EXPORT bool getStormCudaPluginVersionIsDirty();
#endif // STORM_CUDAFORSTORM_VERSION_H_

21
resources/cudaForStorm/storm-cudaplugin-config.h.in
View File

@ -1,21 +0,0 @@
/*
* StoRM - Build-in Options
*
* This file is parsed by CMake during makefile generation
*/
#ifndef STORM_CUDAPLUGIN_GENERATED_STORMCONFIG_H_
#define STORM_CUDAPLUGIN_GENERATED_STORMCONFIG_H_
// Version Information
#define STORM_CUDAPLUGIN_VERSION_MAJOR @STORM_CUDAPLUGIN_VERSION_MAJOR@ // The major version of StoRM
#define STORM_CUDAPLUGIN_VERSION_MINOR @STORM_CUDAPLUGIN_VERSION_MINOR@ // The minor version of StoRM
#define STORM_CUDAPLUGIN_VERSION_PATCH @STORM_CUDAPLUGIN_VERSION_PATCH@ // The patch version of StoRM
#define STORM_CUDAPLUGIN_VERSION_COMMITS_AHEAD @STORM_CUDAPLUGIN_VERSION_COMMITS_AHEAD@ // How many commits passed since the tag was last set
#define STORM_CUDAPLUGIN_VERSION_HASH "@STORM_CUDAPLUGIN_VERSION_HASH@" // The short hash of the git commit this build is bases on
#define STORM_CUDAPLUGIN_VERSION_DIRTY @STORM_CUDAPLUGIN_VERSION_DIRTY@ // 0 iff there no files were modified in the checkout, 1 else
// Whether the size of float in a pair<uint_fast64_t, float> is expanded to 64bit
#@STORM_CUDAPLUGIN_FLOAT_64BIT_ALIGN_DEF@ STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
#endif // STORM_CUDAPLUGIN_GENERATED_STORMCONFIG_H_

4
src/modelchecker/prctl/TopologicalValueIterationMdpPrctlModelChecker.h
View File

@ -15,7 +15,6 @@
namespace storm { namespace storm {
namespace modelchecker { namespace modelchecker {
namespace prctl {
/* /*
* An implementation of the SparseMdpPrctlModelChecker interface that uses topoligical value iteration for solving * An implementation of the SparseMdpPrctlModelChecker interface that uses topoligical value iteration for solving
@ -38,7 +37,7 @@ public:
* Copy constructs a SparseMdpPrctlModelChecker from the given model checker. In particular, this means that the newly * Copy constructs a SparseMdpPrctlModelChecker from the given model checker. In particular, this means that the newly
* constructed model checker will have the model of the given model checker as its associated model. * constructed model checker will have the model of the given model checker as its associated model.
*/ */
explicit TopologicalValueIterationMdpPrctlModelChecker(storm::modelchecker::prctl::TopologicalValueIterationMdpPrctlModelChecker<Type> const& modelchecker)
explicit TopologicalValueIterationMdpPrctlModelChecker(storm::modelchecker::TopologicalValueIterationMdpPrctlModelChecker<Type> const& modelchecker)
: SparseMdpPrctlModelChecker<Type>(modelchecker) { : SparseMdpPrctlModelChecker<Type>(modelchecker) {
// Intentionally left empty. // Intentionally left empty.
} }
@ -49,7 +48,6 @@ public:
virtual ~TopologicalValueIterationMdpPrctlModelChecker() { } virtual ~TopologicalValueIterationMdpPrctlModelChecker() { }
}; };
} // namespace prctl
} // namespace modelchecker } // namespace modelchecker
} // namespace storm } // namespace storm

6
src/solver/TopologicalValueIterationNondeterministicLinearEquationSolver.cpp
View File

@ -36,6 +36,12 @@ namespace storm {
this->maximalNumberOfIterations = settings.getMaximalIterationCount(); this->maximalNumberOfIterations = settings.getMaximalIterationCount();
this->precision = settings.getPrecision(); this->precision = settings.getPrecision();
this->relative = (settings.getConvergenceCriterion() == storm::settings::modules::TopologicalValueIterationEquationSolverSettings::ConvergenceCriterion::Relative); this->relative = (settings.getConvergenceCriterion() == storm::settings::modules::TopologicalValueIterationEquationSolverSettings::ConvergenceCriterion::Relative);
auto generalSettings = storm::settings::generalSettings();
this->enableCuda = generalSettings.isCudaSet();
#ifdef STORM_HAVE_CUDA
STORM_LOG_INFO_COND(this->enableCuda, "Option CUDA was not set, but the topological value iteration solver will use it anyways.");
#endif
} }
template<typename ValueType> template<typename ValueType>

2
src/solver/TopologicalValueIterationNondeterministicLinearEquationSolver.h
View File

@ -44,6 +44,8 @@ namespace storm {
virtual void solveEquationSystem(bool minimize, storm::storage::SparseMatrix<ValueType> const& A, std::vector<ValueType>& x, std::vector<ValueType> const& b, std::vector<ValueType>* multiplyResult = nullptr, std::vector<ValueType>* newX = nullptr) const override; virtual void solveEquationSystem(bool minimize, storm::storage::SparseMatrix<ValueType> const& A, std::vector<ValueType>& x, std::vector<ValueType> const& b, std::vector<ValueType>* multiplyResult = nullptr, std::vector<ValueType>* newX = nullptr) const override;
private: private:
bool enableCuda;
/*! /*!
* Given a topological sort of a SCC Decomposition, this will calculate the optimal grouping of SCCs with respect to the size of the GPU memory. * Given a topological sort of a SCC Decomposition, this will calculate the optimal grouping of SCCs with respect to the size of the GPU memory.
*/ */

59
src/utility/cli.h
View File

@ -25,6 +25,10 @@
#ifdef STORM_HAVE_MSAT #ifdef STORM_HAVE_MSAT
# include "mathsat.h" # include "mathsat.h"
#endif #endif
#ifdef STORM_HAVE_CUDA
#include <cuda.h>
#include <cuda_runtime.h>
#endif
#include "log4cplus/logger.h" #include "log4cplus/logger.h"
#include "log4cplus/loggingmacros.h" #include "log4cplus/loggingmacros.h"
@ -60,6 +64,7 @@ log4cplus::Logger printer;
#include "src/modelchecker/prctl/SparseDtmcPrctlModelChecker.h" #include "src/modelchecker/prctl/SparseDtmcPrctlModelChecker.h"
#include "src/modelchecker/reachability/SparseDtmcEliminationModelChecker.h" #include "src/modelchecker/reachability/SparseDtmcEliminationModelChecker.h"
#include "src/modelchecker/prctl/SparseMdpPrctlModelChecker.h" #include "src/modelchecker/prctl/SparseMdpPrctlModelChecker.h"
#include "src/modelchecker/prctl/TopologicalValueIterationMdpPrctlModelChecker.h"
// Headers for counterexample generation. // Headers for counterexample generation.
#include "src/counterexamples/MILPMinimalLabelSetGenerator.h" #include "src/counterexamples/MILPMinimalLabelSetGenerator.h"
@ -151,6 +156,44 @@ namespace storm {
std::cout << "Linked with " << msatVersion << "." << std::endl; std::cout << "Linked with " << msatVersion << "." << std::endl;
msat_free(msatVersion); msat_free(msatVersion);
#endif #endif
#ifdef STORM_HAVE_CUDA
int deviceCount = 0;
cudaError_t error_id = cudaGetDeviceCount(&deviceCount);
if (error_id == cudaSuccess)
{
std::cout << "Compiled with CUDA support, ";
// This function call returns 0 if there are no CUDA capable devices.
if (deviceCount == 0)
{
std::cout<< "but there are no available device(s) that support CUDA." << std::endl;
} else
{
std::cout << "detected " << deviceCount << " CUDA Capable device(s):" << std::endl;
}
int dev, driverVersion = 0, runtimeVersion = 0;
for (dev = 0; dev < deviceCount; ++dev)
{
cudaSetDevice(dev);
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, dev);
std::cout << "CUDA Device " << dev << ": \"" << deviceProp.name << "\"" << std::endl;
// Console log
cudaDriverGetVersion(&driverVersion);
cudaRuntimeGetVersion(&runtimeVersion);
std::cout << " CUDA Driver Version / Runtime Version " << driverVersion / 1000 << "." << (driverVersion % 100) / 10 << " / " << runtimeVersion / 1000 << "." << (runtimeVersion % 100) / 10 << std::endl;
std::cout << " CUDA Capability Major/Minor version number: " << deviceProp.major<<"."<<deviceProp.minor <<std::endl;
}
std::cout << std::endl;
}
else {
std::cout << "Compiled with CUDA support, but an error occured trying to find CUDA devices." << std::endl;
}
#endif
// "Compute" the command line argument string with which STORM was invoked. // "Compute" the command line argument string with which STORM was invoked.
std::stringstream commandStream; std::stringstream commandStream;
@ -362,9 +405,19 @@ namespace storm {
} }
} }
} else if (model->getType() == storm::models::MDP) { } else if (model->getType() == storm::models::MDP) {
std::shared_ptr<storm::models::Mdp<ValueType>> mdp = model->template as<storm::models::Mdp<ValueType>>();
storm::modelchecker::SparseMdpPrctlModelChecker<ValueType> modelchecker(*mdp);
result = modelchecker.check(*formula.get());
std::shared_ptr<storm::models::Mdp<ValueType>> mdp = model->template as<storm::models::Mdp<ValueType>>();
#ifdef STORM_HAVE_CUDA
if (settings.isCudaSet()) {
storm::modelchecker::TopologicalValueIterationMdpPrctlModelChecker<ValueType> modelchecker(*mdp);
result = modelchecker.check(*formula.get());
} else {
storm::modelchecker::SparseMdpPrctlModelChecker<ValueType> modelchecker(*mdp);
result = modelchecker.check(*formula.get());
}
#else
storm::modelchecker::SparseMdpPrctlModelChecker<ValueType> modelchecker(*mdp);
result = modelchecker.check(*formula.get());
#endif
} }
if (result) { if (result) {

8
test/functional/modelchecker/TopologicalValueIterationMdpPrctlModelCheckerTest.cpp
View File

@ -20,7 +20,7 @@ TEST(TopologicalValueIterationMdpPrctlModelCheckerTest, Dice) {
ASSERT_EQ(mdp->getNumberOfStates(), 169ull); ASSERT_EQ(mdp->getNumberOfStates(), 169ull);
ASSERT_EQ(mdp->getNumberOfTransitions(), 436ull); ASSERT_EQ(mdp->getNumberOfTransitions(), 436ull);
storm::modelchecker::prctl::TopologicalValueIterationMdpPrctlModelChecker<double> mc(*mdp);
storm::modelchecker::TopologicalValueIterationMdpPrctlModelChecker<double> mc(*mdp);
//storm::property::prctl::Ap<double>* apFormula = new storm::property::prctl::Ap<double>("two"); //storm::property::prctl::Ap<double>* apFormula = new storm::property::prctl::Ap<double>("two");
auto apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("two"); auto apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("two");
@ -138,7 +138,7 @@ TEST(TopologicalValueIterationMdpPrctlModelCheckerTest, Dice) {
// ------------- state rewards -------------- // ------------- state rewards --------------
std::shared_ptr<storm::models::Mdp<double>> stateRewardMdp = storm::parser::AutoParser::parseModel(STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.tra", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.lab", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.flip.state.rew", "")->as<storm::models::Mdp<double>>(); std::shared_ptr<storm::models::Mdp<double>> stateRewardMdp = storm::parser::AutoParser::parseModel(STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.tra", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.lab", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.flip.state.rew", "")->as<storm::models::Mdp<double>>();
storm::modelchecker::prctl::TopologicalValueIterationMdpPrctlModelChecker<double> stateRewardModelChecker(*stateRewardMdp);
storm::modelchecker::TopologicalValueIterationMdpPrctlModelChecker<double> stateRewardModelChecker(*stateRewardMdp);
apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("done"); apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("done");
@ -174,7 +174,7 @@ TEST(TopologicalValueIterationMdpPrctlModelCheckerTest, Dice) {
// -------------------------------- state and transition reward ------------------------ // -------------------------------- state and transition reward ------------------------
std::shared_ptr<storm::models::Mdp<double>> stateAndTransitionRewardMdp = storm::parser::AutoParser::parseModel(STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.tra", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.lab", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.flip.state.rew", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.flip.trans.rew")->as<storm::models::Mdp<double>>(); std::shared_ptr<storm::models::Mdp<double>> stateAndTransitionRewardMdp = storm::parser::AutoParser::parseModel(STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.tra", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.lab", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.flip.state.rew", STORM_CPP_BASE_PATH "/examples/mdp/two_dice/two_dice.flip.trans.rew")->as<storm::models::Mdp<double>>();
storm::modelchecker::prctl::TopologicalValueIterationMdpPrctlModelChecker<double> stateAndTransitionRewardModelChecker(*stateAndTransitionRewardMdp);
storm::modelchecker::TopologicalValueIterationMdpPrctlModelChecker<double> stateAndTransitionRewardModelChecker(*stateAndTransitionRewardMdp);
apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("done"); apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("done");
@ -214,7 +214,7 @@ TEST(TopologicalValueIterationMdpPrctlModelCheckerTest, AsynchronousLeader) {
ASSERT_EQ(mdp->getNumberOfStates(), 3172ull); ASSERT_EQ(mdp->getNumberOfStates(), 3172ull);
ASSERT_EQ(mdp->getNumberOfTransitions(), 7144ull); ASSERT_EQ(mdp->getNumberOfTransitions(), 7144ull);
storm::modelchecker::prctl::TopologicalValueIterationMdpPrctlModelChecker<double> mc(*mdp);
storm::modelchecker::TopologicalValueIterationMdpPrctlModelChecker<double> mc(*mdp);
auto apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("elected"); auto apFormula = std::make_shared<storm::logic::AtomicLabelFormula>("elected");
auto eventuallyFormula = std::make_shared<storm::logic::EventuallyFormula>(apFormula); auto eventuallyFormula = std::make_shared<storm::logic::EventuallyFormula>(apFormula);

Write Preview
Loading…
Cancel
Save