cpp-taskflow/example/matrix.cpp at master · guannan-git/cpp-taskflow · GitHub

History

286 lines (221 loc) · 7.19 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

// This program computes the dot products over a set of independent vectors

// and compares the runtime between baseline (sequential), OpenMP, C++ thread,

// and Taskflow implementations.

#include <taskflow/taskflow.hpp>

#include <random>

#include <numeric>

#include <fstream>

using matrix_t = std::vector<std::vector<float>>;

// ----------------------------------------------------------------------------

// Utility section

// ----------------------------------------------------------------------------

// Function: random_matrix

matrix_t random_matrix(size_t N) {

thread_local std::default_random_engine gen(0);

std::normal_distribution<float> d{0.0f, 1.0f};

std::ostringstream oss;

oss << "|----> generating " << N << "x" << N << " matrix by thread "

<< std::this_thread::get_id() << "\n";

std::cout << oss.str();

matrix_t mat(N);

for(auto& r : mat) {

r.resize(N);

for(auto& c : r) {

c = d(gen);

}

return mat;

}

// Operator: multiplication

matrix_t operator * (const matrix_t& A, const matrix_t& B) {

if(A.empty() || B.empty() || A[0].size() != B.size()) {

std::cout << A[0].size() << " " << B.size() << std::endl;

throw std::runtime_error("Dimension mismatched in matrix multiplication\n");

}

size_t M, K, N;

N = A.size();

K = A[0].size();

M = B[0].size();

printf("A[%zux%zu] * B[%zux%zu]\n", N, K, K, M);

// Initialize the matrix

matrix_t ret(N);

for(auto& r : ret) {

r.resize(M);

for(auto& c : r) {

c = 0.0f;

}

// Matrix multiplication

for(size_t i=0; i<N; ++i) {

for(size_t j=0; j<M; ++j) {

for(size_t k=0; k<K; ++k) {

ret[i][j] += A[i][k] * B[k][j];

}

return ret;

}

// ----------------------------------------------------------------------------

// Task section

// ----------------------------------------------------------------------------

// Procedure: baseline

void baseline(const std::vector<size_t>& D) {

std::cout << "========== baseline ==========\n";

auto tbeg = std::chrono::steady_clock::now();

std::cout << "Generating matrix As ...\n";

std::vector<matrix_t> As(D.size());

for(size_t j=0; j<D.size(); ++j) {

As[j] = random_matrix(D[j]);

}

std::cout << "Generating matrix Bs ...\n";

std::vector<matrix_t> Bs(D.size());

for(size_t j=0; j<D.size(); ++j) {

Bs[j] = random_matrix(D[j]);

}

std::cout << "Computing matrix product values Cs ...\n";

std::vector<matrix_t> Cs(D.size());

for(size_t j=0; j<D.size(); ++j) {

Cs[j] = As[j] * Bs[j];

}

auto tend = std::chrono::steady_clock::now();

std::cout << "Baseline takes "

<< std::chrono::duration_cast<std::chrono::milliseconds>(tend-tbeg).count()

<< " ms\n";

}

// Procedure: openmp

void openmp(const std::vector<size_t>& D) {

std::cout << "========== OpenMP ==========\n";

auto tbeg = std::chrono::steady_clock::now();

std::cout << "Generating matrix As ...\n";

std::vector<matrix_t> As(D.size());

#pragma omp parallel for

for(int j=0; j<(int)D.size(); ++j) {

As[j] = random_matrix(D[j]);

}

std::cout << "Generating matrix Bs ...\n";

std::vector<matrix_t> Bs(D.size());

#pragma omp parallel for

for(int j=0; j<(int)D.size(); ++j) {

Bs[j] = random_matrix(D[j]);

}

std::cout << "Computing matrix product values Cs ...\n";

std::vector<matrix_t> Cs(D.size());

#pragma omp parallel for

for(int j=0; j<(int)D.size(); ++j) {

Cs[j] = As[j] * Bs[j];

}

auto tend = std::chrono::steady_clock::now();

std::cout << "OpenMP takes "

<< std::chrono::duration_cast<std::chrono::milliseconds>(tend-tbeg).count()

<< " ms\n";

}

// Procedure: cppthread

void cppthread(const std::vector<size_t>& D) {

std::cout << "========== CppThread ==========\n";

auto tbeg = std::chrono::steady_clock::now();

tf::Threadpool tpl(std::thread::hardware_concurrency());

std::cout << "Generating matrix As ...\n";

std::vector<matrix_t> As(D.size());

std::vector<std::future<void>> futures;

for(size_t j=0; j<D.size(); ++j) {

futures.push_back(tpl.async([&, j] () { As[j] = random_matrix(D[j]); }));

}

std::cout << "Generating matrix Bs ...\n";

std::vector<matrix_t> Bs(D.size());

for(size_t j=0; j<D.size(); ++j) {

futures.push_back(tpl.async([&, j] () { Bs[j] = random_matrix(D[j]); }));

}

std::cout << "Synchronizing As and Bs ...\n";

for(auto& fu : futures) {

fu.get();

}

futures.clear();

std::cout << "Computing matrix product values Cs ...\n";

std::vector<matrix_t> Cs(D.size());

for(size_t j=0; j<D.size(); ++j) {

futures.push_back(tpl.async([&, j] () { Cs[j] = As[j] * Bs[j]; }));

}

std::cout << "Synchronizing Cs ...\n";

for(auto& fu : futures) {

fu.get();

}

auto tend = std::chrono::steady_clock::now();

std::cout << "CppThread takes "

<< std::chrono::duration_cast<std::chrono::milliseconds>(tend-tbeg).count()

<< " ms\n";

}

// Procedure: taskflow

void taskflow(const std::vector<size_t>& D) {

auto tbeg = std::chrono::steady_clock::now();

tf::Taskflow tf;

std::cout << "Generating task As ...\n";

std::vector<matrix_t> As(D.size());

std::vector<tf::Task> TaskAs;

for(size_t j=0; j<D.size(); ++j) {

TaskAs.push_back(tf.silent_emplace([&, j] () {

As[j] = random_matrix(D[j]);

}));

}

std::cout << "Generating task Bs ...\n";

std::vector<matrix_t> Bs(D.size());

std::vector<tf::Task> TaskBs;

for(size_t j=0; j<D.size(); ++j) {

TaskBs.push_back(tf.silent_emplace([&, j] () {

Bs[j] = random_matrix(D[j]);

}));

}

std::cout << "Generating task Cs ...\n";

std::vector<matrix_t> Cs(D.size());

std::vector<tf::Task> TaskCs;

for(size_t j=0; j<D.size(); ++j) {

TaskCs.push_back(tf.silent_emplace([&, j] () {

Cs[j] = As[j] * Bs[j];

}));

}

// Build task dependency

for(size_t j=0; j<D.size(); ++j) {

TaskCs[j].gather({TaskAs[j], TaskBs[j]});

}

tf.wait_for_all();

auto tend = std::chrono::steady_clock::now();

std::cout << "Taskflow takes "

<< std::chrono::duration_cast<std::chrono::milliseconds>(tend-tbeg).count()

<< " ms\n";

}

// ------------------------------------------------------------------------------------------------

// Function: main

int main(int argc, char* argv[]) {

if(argc != 3) {

std::cerr << "usage: ./matrix [baseline|openmp|cppthread|taskflow] N\n";

std::exit(EXIT_FAILURE);

}

// Create a unbalanced dimension for vector products.

const auto N = std::stoul(argv[2]);

std::vector<size_t> dimensions(N);

std::default_random_engine engine(0);

std::uniform_int_distribution dis(1, 1000);

std::cout << "matrix sizes = [";

for(size_t i=0; i<dimensions.size(); ++i) {

dimensions[i] = dis(engine);

if(i) std::cout << ' ';

std::cout << dimensions[i];

}

std::cout << "]\n";

// Run methods

if(std::string_view method(argv[1]); method == "baseline") {

baseline(dimensions);

}

else if(method == "openmp") {

openmp(dimensions);

}

else if(method == "cppthread") {

cppthread(dimensions);

}

else if(method == "taskflow") {

taskflow(dimensions);

}

else {

std::cerr << "wrong method, shoud be [baseline|openmp|cppthread|taskflow]\n";

}

return 0;

}

cpp-taskflow/example/matrix.cpp at master · guannan-git/cpp-taskflow · GitHub

Expand file tree

Breadcrumbs

matrix.cpp

Latest commit

History

Breadcrumbs

matrix.cpp

File metadata and controls