seh*_*ehe 10 c++ parallel-processing multithreading progress openmp
想象一下经典的OMP任务:
using namespace std;
int main() {
vector<double> v;
// generate some data
generate_n(back_inserter(v), 1ul << 18,
bind(uniform_real_distribution<double>(0,1.0), default_random_engine { random_device {}() }));
long double sum = 0;
{
#pragma omp parallel for reduction(+:sum)
for(size_t i = 0; i < v.size(); i++)
{
sum += v[i];
}
}
std::cout << "Done: sum = " << sum << "\n";
}
我无法想出如何报告进度.毕竟,OMP正在为我处理团队线程之间的所有协调,而我没有一个全局状态.
我可能会使用常规std::thread并从那里观察一些共享变量,但是没有更多的"omp-ish"方法来实现这一目标吗？
让团队中的每个线程跟踪本地进度并以原子方式更新全局计数器.您仍然可以让另一个线程观察它,或者,如下面的示例中所示,您可以在OMP关键部分中执行终端输出.
这里的关键是调整不会导致高频率更新的步长,因为关键区域(以及较小程度上的原子加载/存储)的锁定会降低性能.
#include <omp.h>
#include <vector>
#include <random>
#include <algorithm>
#include <iterator>
#include <functional>
#include <iostream>
#include <iomanip>
using namespace std;
int main() {
vector<double> v;
// generate some data
generate_n(back_inserter(v), 1ul << 18, bind(uniform_real_distribution<double>(0,1.0), default_random_engine { random_device {}() }));
auto step_size = 100ul;
auto total_steps = v.size() / step_size + 1;
size_t steps_completed = 0;
long double sum = 0;
#pragma omp parallel
{
size_t local_count = 0;
#pragma omp for reduction(+:sum)
for(size_t i = 0; i < v.size(); i++)
{
sum += v[i];
if (local_count++ % step_size == step_size-1)
{
#pragma omp atomic
++steps_completed;
if (steps_completed % 100 == 1)
{
#pragma omp critical
std::cout << "Progress: " << steps_completed << " of " << total_steps << " (" << std::fixed << std::setprecision(1) << (100.0*steps_completed/total_steps) << "%)\n";
}
}
}
}
std::cout << "Done: sum = " << sum << "\n";
}
最后,打印结果.输出:
Progress: 1 of 2622 (0.0%)
Progress: 191 of 2622 (7.3%)
Progress: 214 of 2622 (8.2%)
Progress: 301 of 2622 (11.5%)
Progress: 401 of 2622 (15.3%)
Progress: 501 of 2622 (19.1%)
Progress: 601 of 2622 (22.9%)
Progress: 701 of 2622 (26.7%)
Progress: 804 of 2622 (30.7%)
Progress: 901 of 2622 (34.4%)
Progress: 1003 of 2622 (38.3%)
Progress: 1101 of 2622 (42.0%)
Progress: 1201 of 2622 (45.8%)
Progress: 1301 of 2622 (49.6%)
Progress: 1402 of 2622 (53.5%)
Progress: 1501 of 2622 (57.2%)
Progress: 1601 of 2622 (61.1%)
Progress: 1701 of 2622 (64.9%)
Progress: 1801 of 2622 (68.7%)
Progress: 1901 of 2622 (72.5%)
Progress: 2001 of 2622 (76.3%)
Progress: 2101 of 2622 (80.1%)
Progress: 2203 of 2622 (84.0%)
Progress: 2301 of 2622 (87.8%)
Progress: 2402 of 2622 (91.6%)
Progress: 2501 of 2622 (95.4%)
Progress: 2601 of 2622 (99.2%)
Done: sum = 130943.8
在没有本机原子支持（甚至有它们）的处理器上#pragma omp atomic，正如这里的其他答案所建议的那样，使用 可能会减慢您的程序速度。
进度指示器的想法是让用户知道某事何时完成。如果您在目标上加上/减去总运行时间的一小部分，用户就不会太烦恼。也就是说，用户希望事情更快完成，但代价是更准确地知道事情何时完成。
出于这个原因，我通常只跟踪单个线程的进度并使用它来估计总进度。这对于每个线程都有相似工作负载的情况来说很好。由于您正在使用#pragma omp parallel for，您可能正在处理一系列没有相互依赖性的类似元素，因此我的假设可能适用于您的用例。
我已经将这个逻辑封装在一个类中ProgressBar，我通常将它和它的辅助类一起包含在一个头文件中Timer。该类使用 ANSI 控制信号来使事情看起来不错。
输出如下所示：
[====== ] (12% - 22.0s - 4 threads)
通过声明-DNOPROGRESS编译标志，让编译器消除进度条的所有开销也很容易。
代码和示例用法如下：
#include <iostream>
#include <chrono>
#include <thread>
#include <iomanip>
#include <stdexcept>
#ifdef _OPENMP
///Multi-threading - yay!
#include <omp.h>
#else
///Macros used to disguise the fact that we do not have multithreading enabled.
#define omp_get_thread_num() 0
#define omp_get_num_threads() 1
#endif
///@brief Used to time how intervals in code.
///
///Such as how long it takes a given function to run, or how long I/O has taken.
class Timer{
private:
typedef std::chrono::high_resolution_clock clock;
typedef std::chrono::duration<double, std::ratio<1> > second;
std::chrono::time_point<clock> start_time; ///< Last time the timer was started
double accumulated_time; ///< Accumulated running time since creation
bool running; ///< True when the timer is running
public:
Timer(){
accumulated_time = 0;
running = false;
}
///Start the timer. Throws an exception if timer was already running.
void start(){
if(running)
throw std::runtime_error("Timer was already started!");
running=true;
start_time = clock::now();
}
///Stop the timer. Throws an exception if timer was already stopped.
///Calling this adds to the timer's accumulated time.
///@return The accumulated time in seconds.
double stop(){
if(!running)
throw std::runtime_error("Timer was already stopped!");
accumulated_time += lap();
running = false;
return accumulated_time;
}
///Returns the timer's accumulated time. Throws an exception if the timer is
///running.
double accumulated(){
if(running)
throw std::runtime_error("Timer is still running!");
return accumulated_time;
}
///Returns the time between when the timer was started and the current
///moment. Throws an exception if the timer is not running.
double lap(){
if(!running)
throw std::runtime_error("Timer was not started!");
return std::chrono::duration_cast<second> (clock::now() - start_time).count();
}
///Stops the timer and resets its accumulated time. No exceptions are thrown
///ever.
void reset(){
accumulated_time = 0;
running = false;
}
};
///@brief Manages a console-based progress bar to keep the user entertained.
///
///Defining the global `NOPROGRESS` will
///disable all progress operations, potentially speeding up a program. The look
///of the progress bar is shown in ProgressBar.hpp.
class ProgressBar{
private:
uint32_t total_work; ///< Total work to be accomplished
uint32_t next_update; ///< Next point to update the visible progress bar
uint32_t call_diff; ///< Interval between updates in work units
uint32_t work_done;
uint16_t old_percent; ///< Old percentage value (aka: should we update the progress bar) TODO: Maybe that we do not need this
Timer timer; ///< Used for generating ETA
///Clear current line on console so a new progress bar can be written
void clearConsoleLine() const {
std::cerr<<"\r\033[2K"<<std::flush;
}
public:
///@brief Start/reset the progress bar.
///@param total_work The amount of work to be completed, usually specified in cells.
void start(uint32_t total_work){
timer = Timer();
timer.start();
this->total_work = total_work;
next_update = 0;
call_diff = total_work/200;
old_percent = 0;
work_done = 0;
clearConsoleLine();
}
///@brief Update the visible progress bar, but only if enough work has been done.
///
///Define the global `NOPROGRESS` flag to prevent this from having an
///effect. Doing so may speed up the program's execution.
void update(uint32_t work_done0){
//Provide simple way of optimizing out progress updates
#ifdef NOPROGRESS
return;
#endif
//Quick return if this isn't the main thread
if(omp_get_thread_num()!=0)
return;
//Update the amount of work done
work_done = work_done0;
//Quick return if insufficient progress has occurred
if(work_done<next_update)
return;
//Update the next time at which we'll do the expensive update stuff
next_update += call_diff;
//Use a uint16_t because using a uint8_t will cause the result to print as a
//character instead of a number
uint16_t percent = (uint8_t)(work_done*omp_get_num_threads()*100/total_work);
//Handle overflows
if(percent>100)
percent=100;
//In the case that there has been no update (which should never be the case,
//actually), skip the expensive screen print
if(percent==old_percent)
return;
//Update old_percent accordingly
old_percent=percent;
//Print an update string which looks like this:
// [================================================ ] (96% - 1.0s - 4 threads)
std::cerr<<"\r\033[2K["
<<std::string(percent/2, '=')<<std::string(50-percent/2, ' ')
<<"] ("
<<percent<<"% - "
<<std::fixed<<std::setprecision(1)<<timer.lap()/percent*(100-percent)
<<"s - "
<<omp_get_num_threads()<< " threads)"<<std::flush;
}
///Increment by one the work done and update the progress bar
ProgressBar& operator++(){
//Quick return if this isn't the main thread
if(omp_get_thread_num()!=0)
return *this;
work_done++;
update(work_done);
return *this;
}
///Stop the progress bar. Throws an exception if it wasn't started.
///@return The number of seconds the progress bar was running.
double stop(){
clearConsoleLine();
timer.stop();
return timer.accumulated();
}
///@return Return the time the progress bar ran for.
double time_it_took(){
return timer.accumulated();
}
uint32_t cellsProcessed() const {
return work_done;
}
};
int main(){
ProgressBar pg;
pg.start(100);
//You should use 'default(none)' by default: be specific about what you're
//sharing
#pragma omp parallel for default(none) schedule(static) shared(pg)
for(int i=0;i<100;i++){
pg.update(i);
std::this_thread::sleep_for(std::chrono::seconds(1));
}
}