Using RcppProgress to control the long computations in C++

Karl Forner — written May 16, 2013 — updated Jul 09, 2017 — Matt Dziubinski — source

Usually you write C++ code with R when you want to speedup some calculations. Depending on the parameters, and especially during the development, it is difficult to anticipate the execution time of your computation, so that you do not know if you have to wait for one minute or several hours.

RcppProgress is a tool to help you monitor the execution time of your C++ code, by providing a way to interrupt the execution inside the C++ code, and also to display a progress bar indicative of the state of your computation.

Additionally, it is compatible with multithreaded code, for example using OpenMP, which is not as trivial as it may seem since you cannot just stop the execution in one thread. Also, not all threads should be writing in the console to avoid garbled output.

// [[Rcpp::depends(RcppProgress)]]
#include <progress.hpp>
// [[Rcpp::export]]
double long_computation(int nb) {
double sum = 0;
for (int i = 0; i < nb; ++i) {
for (int j = 0; j < nb; ++j) {
sum += R::dlnorm(i+j, 0.0, 1.0, 0);
}
}
return sum + nb;
}
    system.time(s  <- long_computation(1000))
   user  system elapsed 
  0.116   0.000   0.114 
    s
[1] 1002.32

Checking for user interrupts

Let’s modify our code to add a check for user interruption by calling the function Progress::check_abort. Note the Rcpp::depends(RcppProgress) attribute in the header part that takes care of the include path for the progress.hpp header.

Now the long_computation2 call should be interruptible (with CTRL+C in the classic R console).

// [[Rcpp::depends(RcppProgress)]]
#include <progress.hpp>
// [[Rcpp::export]]
double long_computation2(int nb) {
double sum = 0;
Progress p(0, false); // we need an instance, should be improved in next version
for (int i = 0; i < nb; ++i) {
if (Progress::check_abort() )
return -1.0;
for (int j = 0; j < nb; ++j) {
sum += R::dlnorm(i+j, 0.0, 1.0, 0);
}
}
return sum + nb;
}
    system.time(s  <- long_computation2(3000)) # interrupt me
   user  system elapsed 
  1.012   0.000   1.022 
    s
[1] 3002.32

You may wonder why we put the check_abort call in the first loop instead that in the second. The performance cost of check_abort call is not negligible. It should be put in a place called often enough (once per second) yet not too often.

Adding a progress bar

Time to add the progress bar. The increment function is quite fast, so we can put it in the second loop. In real life example, it is sufficient to put it at a place called at least every second.

// [[Rcpp::depends(RcppProgress)]]
#include <progress.hpp>
#include <progress_bar.hpp>
// [[Rcpp::export]]
double long_computation3(int nb, bool display_progress=true) {
double sum = 0;
Progress p(nb*nb, display_progress);
for (int i = 0; i < nb; ++i) {
if (Progress::check_abort() )
return -1.0;
for (int j = 0; j < nb; ++j) {
p.increment(); // update progress
sum += R::dlnorm(i+j, 0.0, 1.0, 0);
}
}
return sum + nb;
}
    system.time(s  <- long_computation3(3000)) # interrupt me
   user  system elapsed 
  1.156   0.004   1.196 
    s
[1] 3002.32

OpenMP support

First we need this to enable OpenMP support for gcc. In the early days we used

Sys.setenv("PKG_CXXFLAGS"="-fopenmp")
Sys.setenv("PKG_LIBS"="-fopenmp")

and more recent version of Rcpp have a plugin Recent Rcpp versions should have a plugin which does this for us.

Here is an OpenMP version of our function:

#ifdef _OPENMP
#include <omp.h>
#endif
// [[Rcpp::plugins(openmp)]]
// [[Rcpp::depends(RcppProgress)]]
#include <progress.hpp>
// [[Rcpp::export]]
double long_computation_omp(int nb, int threads=1) {
#ifdef _OPENMP
if ( threads > 0 )
omp_set_num_threads( threads );
REprintf("Number of threads=%i\n", omp_get_max_threads());
#endif

double sum = 0;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < nb; ++i) {
double thread_sum = 0;
for (int j = 0; j < nb; ++j) {
thread_sum += R::dlnorm(i+j, 0.0, 1.0, 0);
}
sum += thread_sum;
}
return sum + nb;
}

Now check that it is parallelized:

    system.time(s4 <- long_computation_omp(5000, 4))
   user  system elapsed 
  2.848   0.004   0.990 
    s4
[1] 5002.14
    system.time(s1 <- long_computation_omp(5000, 1))
   user  system elapsed 
  2.836   0.004   2.851 
    s1
[1] 5002.32

adding progress monitoring to the openMP function

#ifdef _OPENMP
#include <omp.h>
#endif
// [[Rcpp::plugins(openmp)]]
// [[Rcpp::depends(RcppProgress)]]
#include <progress.hpp>
#include <progress_bar.hpp>
// [[Rcpp::export]]
double long_computation_omp2(const int nb, int threads=1) {
#ifdef _OPENMP
if ( threads > 0 )
omp_set_num_threads( threads );
#endif
Progress p(nb, true);
double sum = 0;
#pragma omp parallel for default(none) reduction(+ : sum) schedule(dynamic)
for (int i = 0; i < nb; ++i) {
double thread_sum = 0;
if ( ! Progress::check_abort() ) {
p.increment(); // update progress
for (int j = 0; j < nb; ++j) {
thread_sum += R::dlnorm(i+j, 0.0, 1.0, 0);
}
}
sum += thread_sum;
}
return sum + nb;
}
system.time(s <- long_computation_omp2(5000, 4))

Test it now

If you want to test it now in your R console, just paste the following code (after installing the RcppProgress package, of course):

#ifdef _OPENMP
#include <omp.h>
#endif
// [[Rcpp::plugins(openmp)]]
// [[Rcpp::depends(RcppProgress)]]
#include <progress.hpp>
#include <progress_bar.hpp>

// [[Rcpp::export]]
double long_computation_omp2(int nb, int threads=1) {
#ifdef _OPENMP
if ( threads > 0 )
omp_set_num_threads( threads );
REprintf("Number of threads=%i\\n", omp_get_max_threads());
#endif
Progress p(nb, true);
double sum = 0;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < nb; ++i) {
double thread_sum = 0;
if ( ! Progress::check_abort() ) {
p.increment(); // update progress
for (int j = 0; j < nb; ++j) {
thread_sum += R::dlnorm(i+j, 0.0, 1.0, 0);
}
}
sum += thread_sum;
}

return sum + nb;
}

and run

Rcpp::sourceCpp(code=code)
s <- long_computation_omp2(10000, 4)

Karl Forner
Quartz Bio

tags: interrupt  openmp 

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