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taskflow.cpp
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362 lines (273 loc) · 9.49 KB
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// 2018/10/24 - modified by Tsung-Wei Huang
// - Taskflow is templated at threadpool
// - added graph-level comparison with different thread pools
//
// 2018/09/19 - created by Tsung-Wei Huang
//
// This program is used to benchmark the taskflow under different types
// of workloads.
#include <taskflow/taskflow.hpp>
#include <chrono>
#include <random>
#include <climits>
// Procedure: benchmark
#define BENCHMARK(TITLE, F) \
std::cout << "========== " << TITLE << " ==========\n"; \
\
std::cout << "Taskflow [simple executor ]: " \
<< F<tf::BasicTaskflow<tf::SimpleThreadpool>>() \
<< " ms\n"; \
\
std::cout << "Taskflow [practive executor ]: " \
<< F<tf::BasicTaskflow<tf::ProactiveThreadpool>>() \
<< " ms\n"; \
\
std::cout << "Taskflow [speculative executor]: " \
<< F<tf::BasicTaskflow<tf::SpeculativeThreadpool>>() \
<< " ms\n"; \
// ============================================================================
// Dynamic Stem
// ============================================================================
// Function: dynamic_stem
template <typename T>
auto dynamic_stem() {
auto beg = std::chrono::high_resolution_clock::now();
{
const int L = 1024;
std::atomic<size_t> sum {0};
T tf;
std::optional<tf::Task> prev;
for(int l=0; l<L; ++l) {
auto curr = tf.silent_emplace([&, l] (auto& subflow) {
sum.fetch_add(1, std::memory_order_relaxed);
std::optional<tf::Task> p;
for(int k=0; k<L; k++) {
auto c = subflow.silent_emplace([&] () {
sum.fetch_add(1, std::memory_order_relaxed);
});
if(p) {
p->precede(c);
}
p = c;
}
if(l & 1) {
subflow.detach();
}
});
if(prev) {
prev->precede(curr);
}
prev = curr;
}
tf.wait_for_all();
assert(sum == L*(L+1));
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Map-Reduce
// ============================================================================
// Function: map_reduce
template <typename T>
auto map_reduce() {
auto beg = std::chrono::high_resolution_clock::now();
{
const int num_batches = 65536;
std::vector<int> C(1024, 10);
std::atomic<size_t> sum {0};
T tf;
std::optional<tf::Task> prev;
for(int i=0; i<num_batches; ++i) {
auto [s, t] = tf.parallel_for(C.begin(), C.end(), [&] (int v) {
sum.fetch_add(v, std::memory_order_relaxed);
});
if(prev) {
prev->precede(s);
}
prev = t;
}
tf.wait_for_all();
assert(sum == num_batches * C.size() * 10);
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Level Graph
// ============================================================================
// Function: level_graph
template <typename T>
auto level_graph() {
const int num_levels = 2048;
const int num_nodes_per_level = 1024;
auto beg = std::chrono::high_resolution_clock::now();
{
std::atomic<size_t> sum {0};
T tf;
std::vector< std::vector<tf::Task> > tasks;
tasks.resize(num_levels);
for(int l=0; l<num_levels; ++l) {
for(int i=0; i<num_nodes_per_level; ++i) {
tasks[l].push_back(tf.silent_emplace([&] () {
sum.fetch_add(1, std::memory_order_relaxed);
}));
}
}
// connections for each level l to l+1
for(int l=0; l<num_levels-1; ++l) {
for(int i=0; i<num_nodes_per_level; ++i) {
for(int j=0; j<num_nodes_per_level; j=j+i+1) {
tasks[l][i].precede(tasks[l+1][j]);
}
}
}
tf.wait_for_all();
assert(sum == num_levels * num_nodes_per_level);
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Linear Graph
// ============================================================================
// Function: linear_graph
template <typename T>
auto linear_graph() {
const int num_nodes = 1000000;
auto beg = std::chrono::high_resolution_clock::now();
{
size_t sum {0};
T tf;
std::vector<tf::Task> tasks;
for(int i=0; i<num_nodes; ++i) {
tasks.push_back(tf.silent_emplace([&] () { ++sum; }));
}
tf.linearize(tasks);
tf.wait_for_all();
assert(sum == num_nodes);
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Binary Tree
// ============================================================================
// Function: binary_tree
template <typename T>
auto binary_tree() {
const int num_levels = 21;
auto beg = std::chrono::high_resolution_clock::now();
{
T tf;
std::atomic<size_t> sum {0};
std::function<void(int, tf::Task)> insert;
insert = [&] (int l, tf::Task parent) {
if(l < num_levels) {
auto lc = tf.silent_emplace([&] () {
sum.fetch_add(1, std::memory_order_relaxed);
});
auto rc = tf.silent_emplace([&] () {
sum.fetch_add(1, std::memory_order_relaxed);
});
parent.precede(lc);
parent.precede(rc);
insert(l+1, lc);
insert(l+1, rc);
}
};
auto root = tf.silent_emplace([&] () {
sum.fetch_add(1, std::memory_order_relaxed);
});
insert(1, root);
// synchronize until all tasks finish
tf.wait_for_all();
assert(sum == (1 << (num_levels)) - 1);
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Empty Jobs
// ============================================================================
// Function: empty_jobs
template <typename T>
auto empty_jobs() {
const int num_tasks = 1000000;
auto beg = std::chrono::high_resolution_clock::now();
{
T tf;
for(size_t i=0; i<num_tasks; i++){
tf.silent_emplace([](){});
}
tf.wait_for_all();
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Atomic add
// ============================================================================
// Function: atomic_add
template <typename T>
auto atomic_add() {
const int num_tasks = 1000000;
auto beg = std::chrono::high_resolution_clock::now();
{
std::atomic<int> counter(0);
T tf;
for(size_t i=0; i<num_tasks; i++){
tf.silent_emplace([&counter](){
counter.fetch_add(1, std::memory_order_relaxed);
});
}
tf.wait_for_all();
assert(counter == num_tasks);
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ============================================================================
// Multiple Dispatches
// ============================================================================
// Function: multiple_dispatches
template <typename T>
auto multiple_dispatches() {
auto create_graph = [] (T& tf, size_t N, std::atomic<int>& c) {
for(size_t i=0; i<N; ++i) {
auto [A, B, C, D] = tf.silent_emplace(
[&] () { c.fetch_add(1, std::memory_order_relaxed); },
[&] () { c.fetch_add(1, std::memory_order_relaxed); },
[&] () { c.fetch_add(1, std::memory_order_relaxed); },
[&] () { c.fetch_add(1, std::memory_order_relaxed); }
);
A.precede(B);
C.precede(D);
}
};
auto beg = std::chrono::high_resolution_clock::now();
{
T tf;
for(int p=0; p<1024; ++p) {
std::atomic<int> counter(0);
create_graph(tf, p, counter);
tf.wait_for_all();
assert(counter == p * 4);
}
}
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
}
// ----------------------------------------------------------------------------
// Function: main
int main(int argc, char* argv[]) {
BENCHMARK("Multiple Dispatches", multiple_dispatches);
BENCHMARK("Empty Jobs", empty_jobs);
BENCHMARK("Atomic Add", atomic_add);
BENCHMARK("Binary Tree", binary_tree);
BENCHMARK("Linear Graph", linear_graph);
BENCHMARK("Level Graph", level_graph);
BENCHMARK("Map Reduce", map_reduce);
BENCHMARK("Dynamic Stem", dynamic_stem);
return 0;
}