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