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tsung-wei-huangtsung-wei-huang
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added matrix-multiplication benchmark
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‎CMakeLists.txt‎

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@@ -320,6 +320,23 @@ target_link_libraries(
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)
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set_target_properties(parallel_dnn PROPERTIES COMPILE_FLAGS ${OpenMP_CXX_FLAGS})
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## benchmark 6: matrix multiplication
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message(STATUS "benchmark 6: matrix multiplication")
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set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${TF_BENCHMARK_DIR}/matrix_multiplication)
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add_executable(
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matrix_multiplication
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${TF_BENCHMARK_DIR}/matrix_multiplication/main.cpp
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${TF_BENCHMARK_DIR}/matrix_multiplication/omp.cpp
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${TF_BENCHMARK_DIR}/matrix_multiplication/tbb.cpp
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${TF_BENCHMARK_DIR}/matrix_multiplication/taskflow.cpp
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)
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target_include_directories(matrix_multiplication PRIVATE ${PROJECT_SOURCE_DIR}/3rd-party/CLI11)
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target_link_libraries(
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matrix_multiplication
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${PROJECT_NAME} Threads::Threads ${TBB_IMPORTED_TARGETS} ${OpenMP_CXX_LIBRARIES}
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)
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set_target_properties(matrix_multiplication PROPERTIES COMPILE_FLAGS ${OpenMP_CXX_FLAGS})
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endif()
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# -----------------------------------------------------------------------------

‎benchmark/matrix_multiplication/main.cpp‎

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#include "matrix_multiplication.hpp"
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#include <CLI11.hpp>
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void matrix_multiplication(
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const std::string& model,
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const unsigned num_threads,
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const unsigned num_rounds
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) {
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std::cout << std::setw(12) << "size"
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<< std::setw(12) << "runtime"
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<< std::endl;
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for(int i=128; i<=1024; i += 32) {
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N = i;
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allocate_matrix();
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double runtime {0.0};
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for(unsigned j=0; j<num_rounds; ++j) {
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if(model == "tf") {
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runtime += measure_time_taskflow(num_threads).count();
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}
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else if(model == "tbb") {
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runtime += measure_time_tbb(num_threads).count();
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}
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else if(model == "omp") {
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runtime += measure_time_omp(num_threads).count();
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}
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else assert(false);
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}
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std::cout << std::setw(12) << N
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<< std::setw(12) << runtime / num_rounds / 1e3
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<< std::endl;
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deallocate_matrix();
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}
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}
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int main(int argc, char* argv[]) {
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CLI::App app{"MatrixMultiplication"};
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unsigned num_threads {1};
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app.add_option("-t,--num_threads", num_threads, "number of threads (default=1)");
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unsigned num_rounds {1};
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app.add_option("-r,--num_rounds", num_rounds, "number of rounds (default=1)");
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std::string model = "tf";
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app.add_option("-m,--model", model, "model name tbb|omp|tf (default=tf)")
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->check([] (const std::string& m) {
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if(m != "tbb" && m != "tf" && m != "omp") {
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return "model name should be \"tbb\", \"omp\", or \"tf\"";
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}
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return "";
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});
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CLI11_PARSE(app, argc, argv);
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std::cout << "model=" << model << ' '
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<< "num_threads=" << num_threads << ' '
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<< "num_rounds=" << num_rounds << ' '
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<< std::endl;
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matrix_multiplication(model, num_threads, num_rounds);
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return 0;
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}
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‎benchmark/matrix_multiplication/matrix_multiplication.hpp‎

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#include <algorithm>
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#include <cassert>
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#include <cstdio>
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#include <chrono>
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#include <iostream>
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#include <iomanip>
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#include <thread>
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#include <random>
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#include <cmath>
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#include <atomic>
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inline int N;
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inline double **a, **b, **c;
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std::chrono::microseconds measure_time_taskflow(unsigned);
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std::chrono::microseconds measure_time_tbb(unsigned);
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std::chrono::microseconds measure_time_omp(unsigned);
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inline void allocate_matrix() {
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a = static_cast<double**>(std::malloc(N * sizeof(double*)));
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b = static_cast<double**>(std::malloc(N * sizeof(double*)));
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c = static_cast<double**>(std::malloc(N * sizeof(double*)));
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for(int i=0; i<N; ++i) {
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a[i] = static_cast<double*>(std::malloc(N * sizeof(double)));
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b[i] = static_cast<double*>(std::malloc(N * sizeof(double)));
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c[i] = static_cast<double*>(std::malloc(N * sizeof(double)));
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}
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}
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inline void deallocate_matrix() {
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for(int i=0; i<N; ++i) {
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std::free(a[i]);
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std::free(b[i]);
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std::free(c[i]);
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}
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std::free(a);
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std::free(b);
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std::free(c);
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}
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inline int64_t reduce_sum() {
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int64_t sum {0};
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for(int i=0; i<N; i++) {
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for(int j=0; j<N; ++j) {
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sum += c[i][j];
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}
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}
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return sum;
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}
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‎benchmark/matrix_multiplication/omp.cpp‎

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#include "matrix_multiplication.hpp"
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#include <omp.h>
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// matrix_multiplication_omp
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// reference: https://computing.llnl.gov/tutorials/openMP/samples/C/omp_mm.c
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void matrix_multiplication_omp(unsigned nthreads) {
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omp_set_num_threads(nthreads);
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int i, j, k;
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#pragma omp parallel shared(a, b, c, nthreads) private(i, j, k)
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{
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#pragma omp for schedule (static)
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for (i=0; i<N; i++)
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for (j=0; j<N; j++)
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a[i][j]= i+j;
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#pragma omp for schedule (static)
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for (i=0; i<N; i++)
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for (j=0; j<N; j++)
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b[i][j]= i*j;
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#pragma omp for schedule (static)
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for (i=0; i<N; i++)
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for (j=0; j<N; j++)
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c[i][j]= 0;
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#pragma omp for schedule (static)
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for (i=0; i<N; i++) {
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for(j=0; j<N; j++) {
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for (k=0; k<N; k++) {
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c[i][j] += a[i][k] * b[k][j];
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}
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}
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}
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}
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//std::cout << reduce_sum() << std::endl;
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}
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std::chrono::microseconds measure_time_omp(unsigned num_threads) {
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auto beg = std::chrono::high_resolution_clock::now();
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matrix_multiplication_omp(num_threads);
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auto end = std::chrono::high_resolution_clock::now();
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return std::chrono::duration_cast<std::chrono::microseconds>(end - beg);
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}

‎benchmark/matrix_multiplication/taskflow.cpp‎

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#include "matrix_multiplication.hpp"
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#include <taskflow/taskflow.hpp>
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// matrix_multiplication_taskflow
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void matrix_multiplication_taskflow(unsigned num_threads) {
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tf::Executor executor(num_threads);
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tf::Taskflow taskflow;
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auto pa = taskflow.parallel_for(0, N, 1, [&] (int i) {
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for(int j=0; j<N; ++j) {
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a[i][j] = i + j;
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}
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});
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auto pb = taskflow.parallel_for(0, N, 1, [&] (int i) {
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for(int j=0; j<N; ++j) {
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b[i][j] = i * j;
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}
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});
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auto pc = taskflow.parallel_for(0, N, 1, [&] (int i) {
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for(int j=0; j<N; ++j) {
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c[i][j] = 0;;
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}
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});
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auto pr = taskflow.parallel_for(0, N, 1, [&] (int i) {
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for(int j=0; j<N; ++j) {
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for(int k=0; k<N; k++) {
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c[i][j] += a[i][k] * b[k][j];
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}
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}
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});
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pa.second.precede(pr.first);
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pb.second.precede(pr.first);
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pc.second.precede(pr.first);
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executor.run(taskflow).get();
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//std::cout << reduce_sum() << std::endl;
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}
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std::chrono::microseconds measure_time_taskflow(unsigned num_threads) {
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auto beg = std::chrono::high_resolution_clock::now();
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matrix_multiplication_taskflow(num_threads);
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auto end = std::chrono::high_resolution_clock::now();
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return std::chrono::duration_cast<std::chrono::microseconds>(end - beg);
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}
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‎benchmark/matrix_multiplication/tbb.cpp‎

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#include "matrix_multiplication.hpp"
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#include <tbb/tbb.h>
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#include <tbb/task_scheduler_init.h>
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// matrix_multiplication_tbb
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void matrix_multiplication_tbb(unsigned num_threads) {
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using namespace tbb;
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using namespace tbb::flow;
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tbb::task_scheduler_init init(num_threads);
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parallel_for(0, N, 1, [=](int i) {
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for(int j=0; j<N; ++j) {
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a[i][j] = i + j;
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}
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});
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parallel_for(0, N, 1, [=](int i) {
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for(int j=0; j<N; ++j) {
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b[i][j] = i * j;
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}
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});
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parallel_for(0, N, 1, [=](int i) {
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for(int j=0; j<N; ++j) {
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c[i][j] = 0;
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}
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});
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parallel_for(0, N, 1, [=](int i) {
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for(int j=0; j<N; ++j) {
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for(int k=0; k<N; ++k) {
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c[i][j] += a[i][k] * b[k][j];
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}
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}
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});
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//std::cout << reduce_sum() << std::endl;
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}
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std::chrono::microseconds measure_time_tbb(unsigned num_threads) {
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auto beg = std::chrono::high_resolution_clock::now();
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matrix_multiplication_tbb(num_threads);
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auto end = std::chrono::high_resolution_clock::now();
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return std::chrono::duration_cast<std::chrono::microseconds>(end - beg);
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}

‎docs/master-branch.html‎

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Working Items</h1>
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<ul>
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<li>Improving the performance of work stealing algorithm </li>
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<li>Discovering a scalable memory allocator to handle the taskflow graph</li>
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<li>Discovering a scalable memory allocator to handle the taskflow graph </li>
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<li>Adding more benchmarks to compare Cpp-Taskflow with OpenMP and TBB</li>
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</ul>
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<h1><a class="anchor" id="master-branch_bug_fixes"></a>
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Bug Fixes</h1>

‎doxygen/releases/master-branch.dox‎

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@li Improving the performance of work stealing algorithm
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@li Discovering a scalable memory allocator to handle the taskflow graph
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@li Adding more benchmarks to compare Cpp-Taskflow with OpenMP and TBB
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@section master-branch_bug_fixes Bug Fixes
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example/dataflow.hpp renamed to legacy/20190514/example/dataflow.hpp

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example/dice_pools.cpp renamed to legacy/20190514/example/dice_pools.cpp

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