Browse Source
Fixed include directories for CUDA Plugin in CMakeLists.txt
Fixed include directories for CUDA Plugin in CMakeLists.txt
Refactored all code related to the SPMV kernels to work with float.
Wrote a test that determines whether the compiler uses 64bit boundary alignments on std::pairs of uint64 and float.
Introduced functions that allow for conversions between different ValueTypes (e.g. from float to double and backwards).
Former-commit-id: 830d24064f
tempestpy_adaptions
PBerger
10 years ago
17 changed files with 1388 additions and 368 deletions
-
2CMakeLists.txt
-
31resources/cudaForStorm/CMakeFloatAlignmentCheck.cpp
-
18resources/cudaForStorm/CMakeLists.txt
-
167resources/cudaForStorm/srcCuda/basicValueIteration.cu
-
12resources/cudaForStorm/srcCuda/basicValueIteration.h
-
353resources/cudaForStorm/srcCuda/cuspExtension.h
-
361resources/cudaForStorm/srcCuda/cuspExtensionDouble.h
-
375resources/cudaForStorm/srcCuda/cuspExtensionFloat.h
-
4resources/cudaForStorm/storm-cudaplugin-config.h.in
-
3src/models/PseudoModel.cpp
-
3src/solver/NativeNondeterministicLinearEquationSolver.cpp
-
198src/solver/TopologicalValueIterationNondeterministicLinearEquationSolver.cpp
-
4src/storage/SparseMatrix.cpp
-
17src/storage/SparseMatrix.h
-
1src/storage/StronglyConnectedComponentDecomposition.cpp
-
14src/utility/vector.h
-
193test/functional/solver/CudaPluginTest.cpp
@ -0,0 +1,31 @@ |
|||
/*
|
|||
* 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; |
|||
} |
|||
@ -1,338 +1,47 @@ |
|||
/* |
|||
* 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 <cusp/detail/device/spmv/csr_vector.h> |
|||
#include <limits> |
|||
#include <algorithm> |
|||
|
|||
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 <typename IndexType, typename ValueType, 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(const IndexType num_rows, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndicesAndValues, const ValueType * x, ValueType * y) |
|||
{ |
|||
__shared__ volatile ValueType sdata[VECTORS_PER_BLOCK * THREADS_PER_VECTOR + THREADS_PER_VECTOR / 2]; // padded to avoid reduction conditionals |
|||
__shared__ volatile IndexType ptrs[VECTORS_PER_BLOCK][2]; |
|||
|
|||
const IndexType THREADS_PER_BLOCK = VECTORS_PER_BLOCK * THREADS_PER_VECTOR; |
|||
|
|||
const IndexType thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index |
|||
const IndexType thread_lane = threadIdx.x & (THREADS_PER_VECTOR - 1); // thread index within the vector |
|||
const IndexType vector_id = thread_id / THREADS_PER_VECTOR; // global vector index |
|||
const IndexType vector_lane = threadIdx.x / THREADS_PER_VECTOR; // vector index within the block |
|||
const IndexType num_vectors = VECTORS_PER_BLOCK * gridDim.x; // total number of active vectors |
|||
|
|||
for(IndexType 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 IndexType row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row]; |
|||
const IndexType row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1]; |
|||
|
|||
// initialize local sum |
|||
ValueType sum = 0; |
|||
|
|||
if (THREADS_PER_VECTOR == 32 && row_end - row_start > 32) |
|||
{ |
|||
// ensure aligned memory access to Aj and Ax |
|||
|
|||
IndexType jj = row_start - (row_start & (THREADS_PER_VECTOR - 1)) + thread_lane; |
|||
|
|||
// accumulate local sums |
|||
if(jj >= row_start && jj < row_end) |
|||
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); |
|||
} |
|||
else |
|||
{ |
|||
// accumulate local sums |
|||
for(IndexType 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); |
|||
} |
|||
|
|||
// 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 <typename IndexType, typename ValueType, 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(const IndexType num_rows, const IndexType * nondeterministicChoiceIndices, ValueType * x, const ValueType * y, const ValueType minMaxInitializer) |
|||
{ |
|||
__shared__ volatile ValueType sdata[ROWS_PER_BLOCK * THREADS_PER_ROW + THREADS_PER_ROW / 2]; // padded to avoid reduction conditionals |
|||
__shared__ volatile IndexType ptrs[ROWS_PER_BLOCK][2]; |
|||
|
|||
const IndexType THREADS_PER_BLOCK = ROWS_PER_BLOCK * THREADS_PER_ROW; |
|||
|
|||
const IndexType thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index |
|||
const IndexType thread_lane = threadIdx.x & (THREADS_PER_ROW - 1); // thread index within the vector |
|||
const IndexType vector_id = thread_id / THREADS_PER_ROW; // global vector index |
|||
const IndexType vector_lane = threadIdx.x / THREADS_PER_ROW; // vector index within the block |
|||
const IndexType num_vectors = ROWS_PER_BLOCK * gridDim.x; // total number of active vectors |
|||
|
|||
for(IndexType 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 IndexType row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row]; |
|||
const IndexType row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1]; |
|||
|
|||
// initialize local Min/Max |
|||
ValueType localMinMaxElement = minMaxInitializer; |
|||
|
|||
if (THREADS_PER_ROW == 32 && row_end - row_start > 32) |
|||
{ |
|||
// ensure aligned memory access to Aj and Ax |
|||
|
|||
IndexType jj = row_start - (row_start & (THREADS_PER_ROW - 1)) + thread_lane; |
|||
|
|||
// accumulate local sums |
|||
if(jj >= row_start && jj < row_end) { |
|||
if(Minimize) { |
|||
localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement; |
|||
} else { |
|||
localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement; |
|||
} |
|||
} |
|||
#include "cuspExtensionFloat.h" |
|||
#include "cuspExtensionDouble.h" |
|||
|
|||
// accumulate local sums |
|||
for(jj += THREADS_PER_ROW; jj < row_end; jj += THREADS_PER_ROW) |
|||
if(Minimize) { |
|||
localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement; |
|||
} else { |
|||
localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement; |
|||
} |
|||
} |
|||
else |
|||
{ |
|||
// accumulate local sums |
|||
for(IndexType jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_ROW) |
|||
if(Minimize) { |
|||
localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement; |
|||
} else { |
|||
localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement; |
|||
} |
|||
} |
|||
namespace cusp { |
|||
namespace detail { |
|||
namespace device { |
|||
|
|||
// 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); |
|||
} 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); |
|||
} |
|||
|
|||
// first thread writes the result |
|||
if (thread_lane == 0) |
|||
x[row] = sdata[threadIdx.x]; |
|||
} |
|||
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 <bool Minimize, unsigned int THREADS_PER_VECTOR, typename IndexType, typename ValueType> |
|||
void __storm_cuda_opt_vector_reduce(const IndexType num_rows, const IndexType * nondeterministicChoiceIndices, ValueType * x, const ValueType * y) |
|||
{ |
|||
ValueType __minMaxInitializer = -std::numeric_limits<ValueType>::max(); |
|||
if (Minimize) { |
|||
__minMaxInitializer = std::numeric_limits<ValueType>::max(); |
|||
} |
|||
const ValueType 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<IndexType, ValueType, 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<IndexType, ValueType, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, Minimize> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>> |
|||
(num_rows, nondeterministicChoiceIndices, x, y, minMaxInitializer); |
|||
} |
|||
|
|||
template <bool Minimize, typename IndexType, typename ValueType> |
|||
void storm_cuda_opt_vector_reduce(const IndexType num_rows, const IndexType num_entries, const IndexType * nondeterministicChoiceIndices, ValueType * x, const ValueType * y) |
|||
{ |
|||
const IndexType rows_per_group = num_entries / num_rows; |
|||
|
|||
if (rows_per_group <= 2) { __storm_cuda_opt_vector_reduce<Minimize, 2>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
|||
if (rows_per_group <= 4) { __storm_cuda_opt_vector_reduce<Minimize, 4>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
|||
if (rows_per_group <= 8) { __storm_cuda_opt_vector_reduce<Minimize, 8>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
|||
if (rows_per_group <= 16) { __storm_cuda_opt_vector_reduce<Minimize,16>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
|||
|
|||
__storm_cuda_opt_vector_reduce<Minimize,32>(num_rows, nondeterministicChoiceIndices, x, y); |
|||
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 <bool UseCache, unsigned int THREADS_PER_VECTOR, typename IndexType, typename ValueType> |
|||
void __storm_cuda_opt_spmv_csr_vector(const IndexType num_rows, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndicesAndValues, const ValueType* x, ValueType* 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<IndexType, ValueType, 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<IndexType, ValueType, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>> |
|||
(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); |
|||
|
|||
if (UseCache) |
|||
unbind_x(x); |
|||
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 <typename IndexType, typename ValueType> |
|||
void storm_cuda_opt_spmv_csr_vector(const IndexType num_rows, const IndexType num_entries, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndicesAndValues, const ValueType* x, ValueType* y) |
|||
{ |
|||
const IndexType nnz_per_row = num_entries / num_rows; |
|||
|
|||
if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector<false, 2, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector<false, 4, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector<false, 8, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector<false,16, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
|
|||
__storm_cuda_opt_spmv_csr_vector<false,32, IndexType, ValueType>(num_rows, 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 <typename IndexType, typename ValueType> |
|||
void storm_cuda_opt_spmv_csr_vector_tex(const IndexType num_rows, const IndexType num_entries, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndicesAndValues, const ValueType* x, ValueType* y) |
|||
{ |
|||
const IndexType nnz_per_row = num_entries / num_rows; |
|||
|
|||
if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector<true, 2, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector<true, 4, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector<true, 8, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector<true,16, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
|||
|
|||
__storm_cuda_opt_spmv_csr_vector<true,32, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); |
|||
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); |
|||
} |
|||
|
|||
// NON-OPT |
|||
|
|||
template <bool UseCache, unsigned int THREADS_PER_VECTOR, typename IndexType, typename ValueType> |
|||
void __storm_cuda_spmv_csr_vector(const IndexType num_rows, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndices, const ValueType * matrixValues, const ValueType* x, ValueType* 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<IndexType, ValueType, 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<IndexType, ValueType, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>> |
|||
(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); |
|||
|
|||
if (UseCache) |
|||
unbind_x(x); |
|||
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 <typename IndexType, typename ValueType> |
|||
void storm_cuda_spmv_csr_vector(const IndexType num_rows, const IndexType num_entries, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndices, const ValueType * matrixValues, const ValueType* x, ValueType* y) |
|||
{ |
|||
const IndexType nnz_per_row = num_entries / num_rows; |
|||
|
|||
if (nnz_per_row <= 2) { __storm_cuda_spmv_csr_vector<false, 2>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
if (nnz_per_row <= 4) { __storm_cuda_spmv_csr_vector<false, 4>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
if (nnz_per_row <= 8) { __storm_cuda_spmv_csr_vector<false, 8>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
if (nnz_per_row <= 16) { __storm_cuda_spmv_csr_vector<false,16>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
|
|||
__storm_cuda_spmv_csr_vector<false,32>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, 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 <typename IndexType, typename ValueType> |
|||
void storm_cuda_spmv_csr_vector_tex(const IndexType num_rows, const IndexType num_entries, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndices, const ValueType * matrixValues, const ValueType* x, ValueType* y) |
|||
{ |
|||
const IndexType nnz_per_row = num_entries / num_rows; |
|||
|
|||
if (nnz_per_row <= 2) { __storm_cuda_spmv_csr_vector<true, 2>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
if (nnz_per_row <= 4) { __storm_cuda_spmv_csr_vector<true, 4>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
if (nnz_per_row <= 8) { __storm_cuda_spmv_csr_vector<true, 8>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
if (nnz_per_row <= 16) { __storm_cuda_spmv_csr_vector<true,16>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, x, y); return; } |
|||
|
|||
__storm_cuda_spmv_csr_vector<true,32>(num_rows, matrixRowIndices, matrixColumnIndices, matrixValues, 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 |
|||
|
|||
@ -0,0 +1,361 @@ |
|||
/* |
|||
* 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> |
|||
|
|||
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 * matrixRowIndices, const double * matrixColumnIndicesAndValues, const double * x, double * 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 * nondeterministicChoiceIndices, double * x, const double * 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 |
|||
@ -0,0 +1,375 @@ |
|||
/* |
|||
* 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 |
|||
Write
Preview
Loading…
Cancel
Save
Reference in new issue