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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
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2CMakeLists.txt
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31resources/cudaForStorm/CMakeFloatAlignmentCheck.cpp
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18resources/cudaForStorm/CMakeLists.txt
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143resources/cudaForStorm/srcCuda/basicValueIteration.cu
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12resources/cudaForStorm/srcCuda/basicValueIteration.h
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353resources/cudaForStorm/srcCuda/cuspExtension.h
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361resources/cudaForStorm/srcCuda/cuspExtensionDouble.h
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375resources/cudaForStorm/srcCuda/cuspExtensionFloat.h
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4resources/cudaForStorm/storm-cudaplugin-config.h.in
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3src/models/PseudoModel.cpp
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3src/solver/NativeNondeterministicLinearEquationSolver.cpp
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198src/solver/TopologicalValueIterationNondeterministicLinearEquationSolver.cpp
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4src/storage/SparseMatrix.cpp
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17src/storage/SparseMatrix.h
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1src/storage/StronglyConnectedComponentDecomposition.cpp
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14src/utility/vector.h
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193test/functional/solver/CudaPluginTest.cpp
@ -0,0 +1,31 @@ |
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/*
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* 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 |
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*/ |
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#include <cstdint>
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#include <utility>
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#include <vector>
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#define CONTAINER_SIZE 100ul
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int main(int argc, char* argv[]) { |
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int result = 0; |
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std::vector<std::pair<uint_fast64_t, float>> myVector; |
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for (size_t i = 0; i < CONTAINER_SIZE; ++i) { |
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myVector.push_back(std::make_pair(i, 42.12345f * i)); |
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} |
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char* firstUintPointer = reinterpret_cast<char*>(&(myVector.at(0).first)); |
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char* secondUintPointer = reinterpret_cast<char*>(&(myVector.at(1).first)); |
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ptrdiff_t uintDiff = secondUintPointer - firstUintPointer; |
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if (uintDiff == (2 * sizeof(uint_fast64_t))) { |
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result = 2; |
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} else if (uintDiff == (sizeof(uint_fast64_t) + sizeof(float))) { |
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result = 3; |
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} else { |
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result = -5; |
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} |
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return result; |
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} |
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@ -1,338 +1,47 @@ |
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/* |
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* This is an extension of the original CUSP csr_vector.h SPMV implementation. |
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* It is based on the Code and incorporates changes as to cope with the details |
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* of the StoRM code. |
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* As this is mostly copy & paste, the original license still applies. |
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*/ |
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/* |
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* Copyright 2008-2009 NVIDIA Corporation |
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* |
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* Licensed under the Apache License, Version 2.0 (the "License"); |
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* you may not use this file except in compliance with the License. |
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* You may obtain a copy of the License at |
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* |
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* http://www.apache.org/licenses/LICENSE-2.0 |
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* |
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* Unless required by applicable law or agreed to in writing, software |
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* distributed under the License is distributed on an "AS IS" BASIS, |
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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* See the License for the specific language governing permissions and |
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* limitations under the License. |
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*/ |
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#pragma once |
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#include <cusp/detail/device/spmv/csr_vector.h> |
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#include <limits> |
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#include <algorithm> |
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namespace cusp |
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{ |
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namespace detail |
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{ |
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namespace device |
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{ |
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////////////////////////////////////////////////////////////////////////////// |
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// CSR SpMV kernels based on a vector model (one warp per row) |
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////////////////////////////////////////////////////////////////////////////// |
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// |
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// spmv_csr_vector_device |
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// Each row of the CSR matrix is assigned to a warp. The warp computes |
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// y[i] = A[i,:] * x, i.e. the dot product of the i-th row of A with |
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// the x vector, in parallel. This division of work implies that |
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// the CSR index and data arrays (Aj and Ax) are accessed in a contiguous |
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// manner (but generally not aligned). On GT200 these accesses are |
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// coalesced, unlike kernels based on the one-row-per-thread division of |
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// work. Since an entire 32-thread warp is assigned to each row, many |
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// threads will remain idle when their row contains a small number |
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// of elements. This code relies on implicit synchronization among |
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// threads in a warp. |
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// |
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// spmv_csr_vector_tex_device |
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// Same as spmv_csr_vector_tex_device, except that the texture cache is |
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// used for accessing the x vector. |
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// |
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// Note: THREADS_PER_VECTOR must be one of [2,4,8,16,32] |
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template <typename IndexType, typename ValueType, unsigned int VECTORS_PER_BLOCK, unsigned int THREADS_PER_VECTOR, bool UseCache> |
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__launch_bounds__(VECTORS_PER_BLOCK * THREADS_PER_VECTOR,1) |
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__global__ void |
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storm_cuda_opt_spmv_csr_vector_kernel(const IndexType num_rows, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndicesAndValues, const ValueType * x, ValueType * y) |
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{ |
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__shared__ volatile ValueType sdata[VECTORS_PER_BLOCK * THREADS_PER_VECTOR + THREADS_PER_VECTOR / 2]; // padded to avoid reduction conditionals |
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__shared__ volatile IndexType ptrs[VECTORS_PER_BLOCK][2]; |
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const IndexType THREADS_PER_BLOCK = VECTORS_PER_BLOCK * THREADS_PER_VECTOR; |
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const IndexType thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index |
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const IndexType thread_lane = threadIdx.x & (THREADS_PER_VECTOR - 1); // thread index within the vector |
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const IndexType vector_id = thread_id / THREADS_PER_VECTOR; // global vector index |
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const IndexType vector_lane = threadIdx.x / THREADS_PER_VECTOR; // vector index within the block |
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const IndexType num_vectors = VECTORS_PER_BLOCK * gridDim.x; // total number of active vectors |
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for(IndexType row = vector_id; row < num_rows; row += num_vectors) |
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{ |
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// use two threads to fetch Ap[row] and Ap[row+1] |
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// this is considerably faster than the straightforward version |
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if(thread_lane < 2) |
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ptrs[vector_lane][thread_lane] = matrixRowIndices[row + thread_lane]; |
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const IndexType row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row]; |
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const IndexType row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1]; |
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// initialize local sum |
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ValueType sum = 0; |
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if (THREADS_PER_VECTOR == 32 && row_end - row_start > 32) |
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{ |
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// ensure aligned memory access to Aj and Ax |
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IndexType jj = row_start - (row_start & (THREADS_PER_VECTOR - 1)) + thread_lane; |
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// accumulate local sums |
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if(jj >= row_start && jj < row_end) |
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sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x); |
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// accumulate local sums |
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for(jj += THREADS_PER_VECTOR; jj < row_end; jj += THREADS_PER_VECTOR) |
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sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x); |
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} |
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else |
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{ |
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// accumulate local sums |
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for(IndexType jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_VECTOR) |
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sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x); |
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} |
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// store local sum in shared memory |
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sdata[threadIdx.x] = sum; |
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// reduce local sums to row sum |
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if (THREADS_PER_VECTOR > 16) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 16]; |
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if (THREADS_PER_VECTOR > 8) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 8]; |
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if (THREADS_PER_VECTOR > 4) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 4]; |
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if (THREADS_PER_VECTOR > 2) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 2]; |
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if (THREADS_PER_VECTOR > 1) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 1]; |
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// first thread writes the result |
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if (thread_lane == 0) |
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y[row] = sdata[threadIdx.x]; |
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} |
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} |
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template <typename IndexType, typename ValueType, unsigned int ROWS_PER_BLOCK, unsigned int THREADS_PER_ROW, bool Minimize> |
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__launch_bounds__(ROWS_PER_BLOCK * THREADS_PER_ROW,1) |
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__global__ void |
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storm_cuda_opt_vector_reduce_kernel(const IndexType num_rows, const IndexType * nondeterministicChoiceIndices, ValueType * x, const ValueType * y, const ValueType minMaxInitializer) |
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{ |
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__shared__ volatile ValueType sdata[ROWS_PER_BLOCK * THREADS_PER_ROW + THREADS_PER_ROW / 2]; // padded to avoid reduction conditionals |
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__shared__ volatile IndexType ptrs[ROWS_PER_BLOCK][2]; |
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const IndexType THREADS_PER_BLOCK = ROWS_PER_BLOCK * THREADS_PER_ROW; |
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const IndexType thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index |
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const IndexType thread_lane = threadIdx.x & (THREADS_PER_ROW - 1); // thread index within the vector |
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const IndexType vector_id = thread_id / THREADS_PER_ROW; // global vector index |
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const IndexType vector_lane = threadIdx.x / THREADS_PER_ROW; // vector index within the block |
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const IndexType num_vectors = ROWS_PER_BLOCK * gridDim.x; // total number of active vectors |
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for(IndexType row = vector_id; row < num_rows; row += num_vectors) |
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{ |
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// use two threads to fetch Ap[row] and Ap[row+1] |
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// this is considerably faster than the straightforward version |
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if(thread_lane < 2) |
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ptrs[vector_lane][thread_lane] = nondeterministicChoiceIndices[row + thread_lane]; |
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const IndexType row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row]; |
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const IndexType row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1]; |
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// initialize local Min/Max |
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ValueType localMinMaxElement = minMaxInitializer; |
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if (THREADS_PER_ROW == 32 && row_end - row_start > 32) |
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{ |
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// ensure aligned memory access to Aj and Ax |
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IndexType jj = row_start - (row_start & (THREADS_PER_ROW - 1)) + thread_lane; |
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// accumulate local sums |
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if(jj >= row_start && jj < row_end) { |
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if(Minimize) { |
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localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement; |
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} else { |
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localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement; |
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} |
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} |
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// accumulate local sums |
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for(jj += THREADS_PER_ROW; jj < row_end; jj += THREADS_PER_ROW) |
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if(Minimize) { |
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localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement; |
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} else { |
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localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement; |
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} |
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} |
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else |
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{ |
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// accumulate local sums |
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for(IndexType jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_ROW) |
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if(Minimize) { |
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localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement; |
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} else { |
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localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement; |
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} |
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} |
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#include "cuspExtensionFloat.h" |
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#include "cuspExtensionDouble.h" |
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// store local sum in shared memory |
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sdata[threadIdx.x] = localMinMaxElement; |
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namespace cusp { |
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namespace detail { |
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namespace device { |
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// reduce local min/max to row min/max |
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if (Minimize) { |
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if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement); |
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if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement); |
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if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement); |
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if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement); |
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if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement); |
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} else { |
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if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement); |
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if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement); |
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if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement); |
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if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement); |
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if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement); |
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} |
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// first thread writes the result |
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if (thread_lane == 0) |
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x[row] = sdata[threadIdx.x]; |
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} |
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template <typename ValueType> |
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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) { |
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// |
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throw; |
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} |
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template <bool Minimize, unsigned int THREADS_PER_VECTOR, typename IndexType, typename ValueType> |
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void __storm_cuda_opt_vector_reduce(const IndexType num_rows, const IndexType * nondeterministicChoiceIndices, ValueType * x, const ValueType * y) |
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{ |
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ValueType __minMaxInitializer = -std::numeric_limits<ValueType>::max(); |
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if (Minimize) { |
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__minMaxInitializer = std::numeric_limits<ValueType>::max(); |
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} |
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const ValueType minMaxInitializer = __minMaxInitializer; |
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const size_t THREADS_PER_BLOCK = 128; |
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const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR; |
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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); |
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const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK)); |
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storm_cuda_opt_vector_reduce_kernel<IndexType, ValueType, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, Minimize> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>> |
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(num_rows, nondeterministicChoiceIndices, x, y, minMaxInitializer); |
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template <> |
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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) { |
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storm_cuda_opt_spmv_csr_vector_double(num_rows, num_entries, matrixRowIndices, matrixColumnIndicesAndValues, x, y); |
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} |
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template <bool Minimize, typename IndexType, typename ValueType> |
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void storm_cuda_opt_vector_reduce(const IndexType num_rows, const IndexType num_entries, const IndexType * nondeterministicChoiceIndices, ValueType * x, const ValueType * y) |
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{ |
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const IndexType rows_per_group = num_entries / num_rows; |
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if (rows_per_group <= 2) { __storm_cuda_opt_vector_reduce<Minimize, 2>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
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if (rows_per_group <= 4) { __storm_cuda_opt_vector_reduce<Minimize, 4>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
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if (rows_per_group <= 8) { __storm_cuda_opt_vector_reduce<Minimize, 8>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
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if (rows_per_group <= 16) { __storm_cuda_opt_vector_reduce<Minimize,16>(num_rows, nondeterministicChoiceIndices, x, y); return; } |
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__storm_cuda_opt_vector_reduce<Minimize,32>(num_rows, nondeterministicChoiceIndices, x, y); |
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template <> |
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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) { |
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storm_cuda_opt_spmv_csr_vector_float(num_rows, num_entries, matrixRowIndices, matrixColumnIndicesAndValues, x, y); |
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} |
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template <bool UseCache, unsigned int THREADS_PER_VECTOR, typename IndexType, typename ValueType> |
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void __storm_cuda_opt_spmv_csr_vector(const IndexType num_rows, const IndexType * matrixRowIndices, const IndexType * matrixColumnIndicesAndValues, const ValueType* x, ValueType* y) |
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{ |
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const size_t THREADS_PER_BLOCK = 128; |
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const size_t VECTORS_PER_BLOCK = THREADS_PER_BLOCK / THREADS_PER_VECTOR; |
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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); |
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const size_t NUM_BLOCKS = std::min<size_t>(MAX_BLOCKS, DIVIDE_INTO(num_rows, VECTORS_PER_BLOCK)); |
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if (UseCache) |
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bind_x(x); |
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storm_cuda_opt_spmv_csr_vector_kernel<IndexType, ValueType, VECTORS_PER_BLOCK, THREADS_PER_VECTOR, UseCache> <<<NUM_BLOCKS, THREADS_PER_BLOCK>>> |
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(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); |
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if (UseCache) |
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unbind_x(x); |
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} |
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template <typename IndexType, typename ValueType> |
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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) |
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{ |
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const IndexType nnz_per_row = num_entries / num_rows; |
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if (nnz_per_row <= 2) { __storm_cuda_opt_spmv_csr_vector<false, 2, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
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if (nnz_per_row <= 4) { __storm_cuda_opt_spmv_csr_vector<false, 4, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
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if (nnz_per_row <= 8) { __storm_cuda_opt_spmv_csr_vector<false, 8, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
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if (nnz_per_row <= 16) { __storm_cuda_opt_spmv_csr_vector<false,16, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); return; } |
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__storm_cuda_opt_spmv_csr_vector<false,32, IndexType, ValueType>(num_rows, matrixRowIndices, matrixColumnIndicesAndValues, x, y); |
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template <bool Minimize, typename ValueType> |
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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) { |
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// |
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throw; |
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} |
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template <typename IndexType, typename ValueType> |
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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) |
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{ |
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const IndexType nnz_per_row = num_entries / num_rows; |
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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 |
|||
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