RealtimeRunningData::RealtimeRunningData(const GPS *gps, const Flowmeter *flowmeter) :
QObject(NULL),
m_gps(gps),
m_flowmeter(flowmeter)
{
connect(&m_timer, SIGNAL(timeout()), SLOT(produceData()));
}
int main(int argc, char *argv[])
{
MatMulArgs matMulArgs;
matMulArgs.processArgs(argc, argv);
size_t matrixAHeight = matMulArgs.getMatrixAHeight();
size_t matrixBWidth = matMulArgs.getMatrixBWidth();
size_t sharedDim = matMulArgs.getSharedDim();
size_t blockSize = matMulArgs.getBlockSize();
size_t numReadThreads = matMulArgs.getNumReadThreads();
size_t numProdThreads = matMulArgs.getNumMatMulThreads();
size_t numAccumThreads = (size_t) ceil((double)numProdThreads / 2.0);
std::string directory = matMulArgs.getDirectory();
std::string outputDirectory = matMulArgs.getOutputDir();
bool runSequential = matMulArgs.isRunSequential();
bool validate = matMulArgs.isValidateResults();
size_t numGPUs = matMulArgs.getNumGPUs();
int gpuIds[numGPUs];
matMulArgs.copyGpuIds(gpuIds);
// CUcontext *contexts = initCuda(numGPUs, gpuIds);
std::string runtimeFileStr("runtimes");
int numRetry = 1;
std::ofstream runtimeFile(runtimeFileStr, std::ios::app);
double *matrixA = new double[matrixAHeight * sharedDim];
double *matrixB = new double[matrixBWidth * sharedDim];
double *matrixC = new double[matrixAHeight * matrixBWidth];
initMatrix(matrixA, sharedDim, matrixAHeight, true);
initMatrix(matrixB, matrixBWidth, sharedDim, true);
for (int numTry = 0; numTry < numRetry; numTry++) {
SimpleClock clk;
SimpleClock endToEnd;
if (runSequential) {
endToEnd.start();
initMatMul(numProdThreads);
cublasXtHandle_t handle;
cublasXtCreate(&handle);
cublasXtDeviceSelect(handle, numGPUs, gpuIds);
cublasXtSetBlockDim(handle, blockSize);
clk.start();
computeSequentialMatMul(matrixA, matrixB, matrixC, (size_t) matrixAHeight, (size_t) sharedDim,
(size_t) matrixBWidth, handle);
clk.stopAndIncrement();
cublasXtDestroy(handle);
endToEnd.stopAndIncrement();
}
else {
endToEnd.start();
initMatMul(1);
LoadMatrixTask *readAMatTask =
new LoadMatrixTask(matrixA,
numReadThreads,
MatrixType::MatrixA,
blockSize,
sharedDim,
matrixAHeight,
true);
LoadMatrixTask *readBMatTask =
new LoadMatrixTask(matrixB,
numReadThreads,
MatrixType::MatrixB,
blockSize,
matrixBWidth,
sharedDim,
true);
MatrixMulBlkCudaTask *mmulTask = new MatrixMulBlkCudaTask(gpuIds, numGPUs);
MatMulAccumTask *accumTask = new MatMulAccumTask(numAccumThreads, true);
MatMulOutputTask *outputTask = new MatMulOutputTask(matrixC, matrixAHeight, blockSize, true);
size_t blkHeightMatB = readBMatTask->getNumBlocksRows();
size_t blkWidthMatB = readBMatTask->getNumBlocksCols();
size_t blkHeightMatA = readAMatTask->getNumBlocksRows();
size_t blkWidthMatA = readAMatTask->getNumBlocksCols();
CudaCopyInTask *cudaCopyInATask = new CudaCopyInTask(gpuIds, numGPUs, MatrixType::MatrixA, blkWidthMatB);
CudaCopyInTask *cudaCopyInBTask = new CudaCopyInTask(gpuIds, numGPUs, MatrixType::MatrixB, blkHeightMatA);
CudaCopyOutTask *cudaCopyOutCTask = new CudaCopyOutTask(gpuIds, numGPUs, MatrixType::MatrixC);
MatMulDistributeRule *distributeRuleMatA = new MatMulDistributeRule(MatrixType::MatrixA);
MatMulDistributeRule *distributeRuleMatB = new MatMulDistributeRule(MatrixType::MatrixB);
MatMulLoadRule<htgs::m_data_t<double>> *loadRule = new MatMulLoadRule<htgs::m_data_t<double>>(blkWidthMatA, blkHeightMatA, blkWidthMatB, blkHeightMatB);
MatMulAccumulateRule<double *> *accumulateRule = new MatMulAccumulateRule<double *>(blkWidthMatB, blkHeightMatA, blkWidthMatA);
MatMulOutputRule *outputRule = new MatMulOutputRule(blkWidthMatB, blkHeightMatA, blkWidthMatA);
auto distributeBk = new htgs::Bookkeeper<MatrixRequestData>();
auto matMulBk = new htgs::Bookkeeper<MatrixBlockData<htgs::m_data_t<double>>>();
auto matAccumBk = new htgs::Bookkeeper<MatrixBlockData<double *>>();
auto taskGraph = new htgs::TaskGraphConf<MatrixRequestData, MatrixBlockData<double *>>();
taskGraph->setGraphConsumerTask(distributeBk);
taskGraph->addRuleEdge(distributeBk, distributeRuleMatA, readAMatTask);
taskGraph->addRuleEdge(distributeBk, distributeRuleMatB, readBMatTask);
taskGraph->addEdge(readAMatTask, cudaCopyInATask);
taskGraph->addEdge(readBMatTask, cudaCopyInBTask);
taskGraph->addEdge(cudaCopyInATask, matMulBk);
taskGraph->addEdge(cudaCopyInBTask, matMulBk);
taskGraph->addRuleEdge(matMulBk, loadRule, mmulTask);
taskGraph->addEdge(mmulTask, cudaCopyOutCTask);
taskGraph->addGraphProducerTask(cudaCopyOutCTask);
taskGraph->addCudaMemoryManagerEdge(matrixTypeToString(MatrixType::MatrixA) + "Copy",
cudaCopyInATask,
new CudaAllocator(blockSize, blockSize),
blkWidthMatB+1,
htgs::MMType::Static,
gpuIds);
taskGraph->addCudaMemoryManagerEdge(matrixTypeToString(MatrixType::MatrixB) + "Copy",
cudaCopyInBTask,
new CudaAllocator(blockSize, blockSize),
blkHeightMatA+1,
htgs::MMType::Static,
gpuIds);
taskGraph->addCudaMemoryManagerEdge(matrixTypeToString(MatrixType::MatrixC),
mmulTask,
new CudaAllocator(blockSize, blockSize),
4,
htgs::MMType::Static,
gpuIds);
auto mainTaskGraph = new htgs::TaskGraphConf<MatrixRequestData, MatrixRequestData>();
auto execPipeline = new htgs::ExecutionPipeline<MatrixRequestData, MatrixBlockData<double *>>(numGPUs, taskGraph);
auto decompositionRule = new MatrixDecompositionRule(numGPUs);
execPipeline->addInputRule(decompositionRule);
mainTaskGraph->setGraphConsumerTask(execPipeline);
mainTaskGraph->addEdge(execPipeline, matAccumBk);
mainTaskGraph->addRuleEdge(matAccumBk, outputRule, outputTask);
mainTaskGraph->addRuleEdge(matAccumBk, accumulateRule, accumTask);
mainTaskGraph->addEdge(accumTask, matAccumBk);
mainTaskGraph->addGraphProducerTask(outputTask);
// mainTaskGraph->writeDotToFile("pre-execution.dot");
htgs::TaskGraphRuntime *runtime = new htgs::TaskGraphRuntime(mainTaskGraph);
clk.start();
runtime->executeRuntime();
for (size_t col = 0; col < blkWidthMatA; col++) {
for (size_t row = 0; row < blkHeightMatA; row++) {
MatrixRequestData *matA = new MatrixRequestData(row, col, MatrixType::MatrixA);
mainTaskGraph->produceData(matA);
}
}
for (size_t row = 0; row < blkHeightMatB; row++) {
for (size_t col = 0; col < blkWidthMatB; col++) {
MatrixRequestData *matB = new MatrixRequestData(row, col, MatrixType::MatrixB);
mainTaskGraph->produceData(matB);
}
}
mainTaskGraph->finishedProducingData();
while (!mainTaskGraph->isOutputTerminated()) {
auto data = mainTaskGraph->consumeData();
if (data != nullptr) {
// std::cout << data->getRow() << ", " << data->getCol() << std::endl;
}
}
runtime->waitForRuntime();
// taskGraph->writeDotToFile("profile-graph.dot");
// mainTaskGraph->writeDotToFile("profile-all-threads-graph.dot", DOTGEN_FLAG_SHOW_ALL_THREADING);
mainTaskGraph->writeDotToFile("matrix-multiplication-cuda-multigpu.dot", DOTGEN_COLOR_COMP_TIME);
clk.stopAndIncrement();
delete runtime;
endToEnd.stopAndIncrement();
}
if (validate) {
double *matrixCTest = new double[matrixAHeight * matrixBWidth];
initMatMul(numProdThreads);
cublasXtHandle_t handle;
cublasXtCreate(&handle);
cublasXtDeviceSelect(handle, (int)numGPUs, gpuIds);
cublasXtSetBlockDim(handle, (int)blockSize);
computeSequentialMatMul(matrixA, matrixB, matrixCTest, (size_t) matrixAHeight, (size_t) sharedDim,
(size_t) matrixBWidth, handle);
cublasXtDestroy(handle);
int res = validateResults(matrixC, matrixCTest, matrixAHeight, matrixBWidth);
if (res != 0) {
std::cout << "Error validating test failed!" << std::endl;
}
else
{
std::cout << "Test PASSED" << std::endl;
}
delete []matrixCTest;
}
double numGflops = (2.0 * matrixAHeight *sharedDim * matrixBWidth) * 1.0e-9d;
double gflops = numGflops / clk.getAverageTime(TimeVal::SEC);
std::cout << (runSequential ? "sequential" : "htgs") << ", " << numProdThreads
<< ", accum-threads: " << numAccumThreads << ", width-b: " << matrixBWidth << ", height-a: " << matrixAHeight
<< ", shared-dim: " << sharedDim
<< ", blockSize: " << blockSize
<< ", time:" << clk.getAverageTime(TimeVal::MILLI)
<< ", end-to-end:" << endToEnd.getAverageTime(TimeVal::MILLI)
<< ", gflops: " << gflops
<< std::endl;
runtimeFile << "MULTIGPU-MM" << (runSequential ? "sequential" : "htgs") << ", " << numProdThreads
<< ", " << numAccumThreads << ", "
<< matrixBWidth << ", " << matrixAHeight
<< ", " << sharedDim << ", " << blockSize << ", " << clk.getAverageTime(TimeVal::MILLI)
<< ", " << endToEnd.getAverageTime(TimeVal::MILLI)
<< std::endl;
}
delete[] matrixA;
delete[] matrixB;
delete[] matrixC;
}
int main(int argc, char *argv[]) {
long matrixSize= 16384;
int blockSize = 128;
bool runSequential = false;
bool validate = false;
int numBlasThreads = 40;
int numGausElimThreads = 2;
int numFactorLowerThreads = 4;
int numFactorUpperThreads = 4;
int numMatrixMulThreads = 30;
std::string runtimeFileStr("runtimes");
int numRetry = 1;
if (argc > 1) {
for (int arg = 1; arg < argc; arg++) {
std::string argvs(argv[arg]);
if (argvs == "--size") {
arg++;
matrixSize = atoi(argv[arg]);
}
if (argvs == "--num-threads-blas") {
arg++;
numBlasThreads = atoi(argv[arg]);
}
if (argvs == "num-threads-factor-l") {
arg++;
numFactorLowerThreads = atoi(argv[arg]);
}
if (argvs == "num-threads-factor-u") {
arg++;
numFactorUpperThreads = atoi(argv[arg]);
}
if (argvs == "num-threads-gaus") {
arg++;
numGausElimThreads = atoi(argv[arg]);
}
if (argvs == "num-threads-gemm") {
arg++;
numMatrixMulThreads = atoi(argv[arg]);
}
if (argvs == "--run-sequential") {
runSequential = true;
}
if (argvs == "--num-retry" && arg + 1 < argc) {
arg++;
numRetry = atoi(argv[arg]);
}
if (argvs == "--block-size") {
arg++;
blockSize = atoi(argv[arg]);
}
if (argvs == "--runtime-file" && arg + 1 < argc) {
runtimeFileStr = argv[arg + 1];
arg++;
}
if (argvs == "--validate-results") {
validate = true;
}
if (argvs == "--help") {
std::cout << argv[0]
<< " args: [--size <#>] [--block-size <#>] [--num-retry <#>] [--runtime-file <filename>] [--validate-results] [--run-sequential] [--num-threads-factor-l <#>] [--num-threads-factor-u <#>] [--num-threads-gaus <#>] [--num-threads-gemm <#>] [--num-threads-blas <#>] [--help]"
<< std::endl;
exit(0);
}
}
}
std::ofstream runtimeFile(runtimeFileStr, std::ios::app);
double *matrix = new double[matrixSize * matrixSize];
double *matrixTest = nullptr;
// TODO: Ensure diagonally dominant
initMatrixDiagDom(matrix, matrixSize, matrixSize, true);
if (validate) {
matrixTest = new double[matrixSize * matrixSize];
for (int i = 0; i < matrixSize * matrixSize; i++)
matrixTest[i] = matrix[i];
}
for (int numTry = 0; numTry < numRetry; numTry++) {
SimpleClock clk;
SimpleClock endToEnd;
if (runSequential) {
endToEnd.start();
mkl_domain_set_num_threads(numBlasThreads, MKL_DOMAIN_ALL);
// mkl_set_num_threads(40);
clk.start();
runSequentialLU(matrix, matrixSize);
// computeSequentialMatMul(matrixA, matrixB, matrixC, matrixAHeight, sharedDim, matrixBWidth);
clk.stopAndIncrement();
endToEnd.stopAndIncrement();
}
else {
endToEnd.start();
mkl_domain_set_num_threads(numBlasThreads, MKL_DOMAIN_ALL);
int gridHeight = (int) matrixSize / blockSize;
int gridWidth = (int) matrixSize / blockSize;
// TODO: Build graph and runtime
htgs::StateContainer<std::shared_ptr<MatrixBlockData<double *>>> *matrixBlocks = new htgs::StateContainer<std::shared_ptr<MatrixBlockData<double *>>>(gridHeight, gridWidth, nullptr);
for (int r = 0; r < gridHeight; r++)
{
for (int c = 0; c < gridWidth; c++)
{
// Store pointer locations for all blocks
double *ptr = &matrix[IDX2C(r * blockSize, c *blockSize, matrixSize)];
std::shared_ptr<MatrixRequestData> request(new MatrixRequestData(r, c, MatrixType::MatrixA));
std::shared_ptr<MatrixBlockData<double *>> data(new MatrixBlockData<double *>(request, ptr, blockSize, blockSize));
matrixBlocks->set(r, c, data);
}
}
GausElimTask *gausElimTask = new GausElimTask(numGausElimThreads, matrixSize, matrixSize);
auto gausElimBk = new htgs::Bookkeeper<MatrixBlockData<double *>>();
GausElimRuleUpper *gausElimRuleUpper = new GausElimRuleUpper(matrixBlocks, gridHeight, gridWidth);
GausElimRuleLower *gausElimRuleLower = new GausElimRuleLower(matrixBlocks, gridHeight, gridWidth);
FactorUpperTask *factorUpperTask = new FactorUpperTask(numFactorUpperThreads, matrixSize, matrixSize);
FactorLowerTask *factorLowerTask = new FactorLowerTask(numFactorLowerThreads, matrixSize, matrixSize);
auto matrixMulBk = new htgs::Bookkeeper<MatrixBlockData<double *>>();
MatrixMulRule *matrixMulRule = new MatrixMulRule(matrixBlocks, gridHeight, gridWidth);
MatrixMulBlkTask *matrixMulTask = new MatrixMulBlkTask(numMatrixMulThreads, matrixSize, matrixSize, matrixSize, matrixSize, blockSize);
auto matrixMulResultBk = new htgs::Bookkeeper<MatrixBlockData<double *>>();
int numDiagonals = gridWidth - 1;
GausElimRule *gausElimRule = new GausElimRule(numDiagonals, gridHeight, gridWidth);
// Number of updates excluding the diagonal and the top/left row/column
int numUpdates = (1.0/6.0) * (double)gridWidth * (2.0 * ((double)gridWidth * (double)gridWidth) - 3.0 * (double)gridWidth + 1.0);
UpdateRule *updateRule = new UpdateRule(numUpdates);
UpdateRule *updateRule2 = new UpdateRule(numUpdates);
auto taskGraph = new htgs::TaskGraph<MatrixBlockData<double *>, htgs::VoidData>();
taskGraph->addGraphInputConsumer(gausElimTask);
taskGraph->addEdge(gausElimTask, gausElimBk);
taskGraph->addRule(gausElimBk, factorUpperTask, gausElimRuleUpper);
taskGraph->addRule(gausElimBk, factorLowerTask, gausElimRuleLower);
taskGraph->addEdge(factorUpperTask, matrixMulBk);
taskGraph->addEdge(factorLowerTask, matrixMulBk);
taskGraph->addRule(matrixMulBk, matrixMulTask, matrixMulRule);
taskGraph->addEdge(matrixMulTask, matrixMulResultBk);
if (numDiagonals > 0)
taskGraph->addRule(matrixMulResultBk, gausElimTask, gausElimRule);
if (numUpdates > 0)
taskGraph->addRule(matrixMulResultBk, matrixMulBk, updateRule);
if (numUpdates > 0)
taskGraph->addRule(matrixMulResultBk, gausElimBk, updateRule2);
taskGraph->incrementGraphInputProducer();
taskGraph->writeDotToFile("lud-graph.dot");
htgs::Runtime *runtime = new htgs::Runtime(taskGraph);
clk.start();
runtime->executeRuntime();
taskGraph->produceData(matrixBlocks->get(0, 0));
taskGraph->finishedProducingData();
runtime->waitForRuntime();
clk.stopAndIncrement();
delete runtime;
endToEnd.stopAndIncrement();
}
double operations = (2.0 * (matrixSize * matrixSize * matrixSize)) / 3.0;
double flops = operations / clk.getAverageTime(TimeVal::SEC);
double gflops = flops / 1073741824.0;
std::cout << (runSequential ? "sequential" : "htgs")
<< ", matrix-size: " << matrixSize
<< ", " << "blockSize: " << (runSequential ? 0 : blockSize)
<< ", blasThreads: " << numBlasThreads
<< ", gausThreads: " << numGausElimThreads
<< ", factorUpperThreads: " << numFactorUpperThreads
<< ", factorLowerThreads: " << numFactorLowerThreads
<< ", gemmThreads: " << numMatrixMulThreads
<< ", time:" << clk.getAverageTime(TimeVal::MILLI)
<< ", end-to-end:" << endToEnd.getAverageTime(TimeVal::MILLI)
<< ", gflops: " << gflops
<< std::endl;
runtimeFile << (runSequential ? "sequential" : "htgs")
<< ", " << matrixSize
<< ", " << blockSize
<< ", " << numBlasThreads
<< ", " << numGausElimThreads
<< ", " << numFactorUpperThreads
<< ", " << numFactorLowerThreads
<< ", " << numMatrixMulThreads
<< ", " << clk.getAverageTime(TimeVal::MILLI)
<< ", " << endToEnd.getAverageTime(TimeVal::MILLI)
<< ", " << gflops
<< std::endl;
if (validate)
{
int res = validateResults(matrix, matrixTest, matrixSize);
std::cout << (res == 0 ? "PASSED" : "FAILED") << std::endl;
}
}
delete[] matrix;
delete[] matrixTest;
}