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Our project: C++11 Threads Library Comparison to OpenMP
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Oct 17th:
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Oct 20th:
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OpenMp vs C++ 11 Threads
What are C++ 11 Threads
With the introduction of C++ 11, there were major changes and additions made to the C++ Standard libraries. One of the most significant changes was the inclusion of multi-threading libraries. Before C++ 11 in order to implement multi-threading, external libraries or language extensions such as OpenMp was required. Not only the standard library now include support for multi-threading, it also offered synchronization and thread safety. Two options were offered for multi-threading,Synchronous threading via std::thread and Asynchronous threading via std::async and std::future.
The C++ 11 thread support library includes these 4 files to enable multi-threading
- <thread> - class and namespace for working with threads
- <mutex> - provides support for mutual exclusion
- <contition_variable> - a synchronization primitive that can be used to block a thread, or multiple threads at the same time, until another thread both modifies a shared variable (the condition), and notifies the condition_variable.
- <future> - Describes components that a C++ program can use to retrieve in one thread the result (value or exception) from a function that has run in the same thread or another thread.
Creating and executing Threads
Inside a declared OpenMp parallel region, if not specified via an environment variable OMP_NUM_THREADS or the library routine omp_get_thread_num() , OpenMp will automatically decide how many threads are needed to execute parallel code. An issue with this approach is that OpenMp is unaware how many threads a CPU can support. A result of this can be OpenMp creating 4 threads for a single core processor which may result in a degradation of performance.
Automatic thread creation
#pragma omp parallel
{
int tid = omp_get_thread_num();
std::cout << "Hi from thread "
<< tid << '\n';
}
Programmer Specified thread creation
int numThreads = 4;
omp_set_num_threads(numThreads);
#pragma omp parallel
{
int tid = omp_get_thread_num();
std::cout << "Hi from thread "
<< tid << '\n';
}
C++ 11 Threads on the contrary always required to specify the number of threads required for a parallel region. If not specified by user input or hardcoding, the number of threads supported by a CPU can also be accurately via the std::thread::hardware_concurrency(); function. OpenMp automatically decides what order threads will execute. C++ 11 Threads require the developer to specify in what order threads will execute. This is typically done within a for loop block.
Native Threads creation
int numThreads = std::thread::hardware_concurrency();
std::vector<std::thread> threads(numThreads);
for (int ID = 0; ID < numThreads; ID++) {
threads[ID] = std::thread(function);
}
Parallelizing for Loops
In OpenMp, paralleling for loops can be accomplished using SPMD or Work-Sharing. When using work-sharing, the omp for construct makes parallelizing for loops a straight-forward and simple process.
By placing the appropriate #pragma omp construct over the loop to be parallelized, the range for distributing work across multiple threads is automatically calculated by OpenMp. All that is required to use the omp for construct is to remove any possible data-dependencies within the parallel region.
C++ 11 threads and language native threads unfortunately lack this luxury. In order to parallelize a loop using std Threads, it is the programmers responsibility to calculate the range of each iteration within the loop the be parallelized. This is usually done using SPMD techniques.
Mutual Exclusion
OpenMp
C++ 11
The C++ 11 thread libraries provide the mutex and the atomic classes which support mutual exclusion.
The mutex class is a synchronization primitive that can be used to protect shared data from being accessed by multiple threads.
std::mutex is usually not accessed directly, instead std::unique_lock and std::lock_guard are used to manage locking.
Mutex offers these member functions for controlling locking
• lock - locks the mutex, blocks if the mutex is not availabl
• unlock - unlocks the mutex
• try_lock - tries to lock the mutex, returns if the mutex is not available
The atomic class provides an atomic object type which can eliminate the possibility of data races by providing synchronization between threads. Accesses to atomic objects may establish inter-thread synchronization and order non-atomic memory accesses.
Atomic types are defined as
std::atomic<type> var_name;
Implementations
Serial Implementation
#include <iostream>
#include <chrono>
using namespace std::chrono;
int main(int argc, char *argv[])
{
steady_clock::time_point ts, te;
const size_t n = 100000000;
int j = 0;
ts = steady_clock::now();
for (int i = 0; i<n; i++)
{
j += i;
}
te = steady_clock::now();
std::cout << j << std::endl;
auto ms = duration_cast<milliseconds>(te - ts);
std::cout << std::endl << "Took - " <<
ms.count() << " milliseconds" << std::endl;
}
Finished at 180 milliseconds
OpenMp with work-sharing implementation
#include <iostream>
#include <chrono>
#include <omp.h>
using namespace std::chrono;
int main(int argc, char *argv[])
{
const size_t n = 100000000;
steady_clock::time_point ts, te;
int j = 0;
int i;
ts = steady_clock::now();
#pragma omp parallel num_threads(8)
{
#pragma omp for reduction(+:j)
for (i = 0; i < n; i++){
j += i;
}
}
te = steady_clock::now();
std::cout << j << std::endl;
auto ms = duration_cast<milliseconds>(te - ts);
std::cout << std::endl << "Took - " <<
ms.count() << " milliseconds" << std::endl;
}
Finished at 63 milliseconds
Native SPMD Implementation using mutex locking barrier. std::bind() allows the user to specify the range for each thread.
#include <iostream>
#include <chrono>
#include <vector>
#include <thread>
#include <mutex>
#include <algorithm>
using namespace std::chrono;
int main(int argc, char *argv[]){
const size_t n = 100000000;
steady_clock::time_point ts, te;
const size_t nthreads = std::thread::hardware_concurrency();
std::vector<std::thread> threads(nthreads);
std::mutex critical;
int j = 0;
ts = steady_clock::now();
for (int t = 0; t < nthreads; t++)
{
threads[t] = std::thread(std::bind([&](const int bi, const int ei, const int t)
{
std::lock_guard<std::mutex> lock(critical);
for (int i = bi; i < ei; i++)
{
j += i;
}
},t*n / nthreads, (t + 1) == nthreads ? n : (t + 1)*n / nthreads, t));
}
te = steady_clock::now();
std::for_each(threads.begin(), threads.end(), [](std::thread& x){x.join(); });
std::cout << j << std::endl;
auto ms = duration_cast<milliseconds>(te - ts);
std::cout << std::endl << "Took - " <<
ms.count() << " milliseconds" << std::endl;
}
Finished at 6 milliseconds
Programming Models
SPMD
An example of the SPMD programming model in STD Threads using an atomic barrier
#include <iostream>
#include <iomanip>
#include <cstdlib>
#include <chrono>
#include <vector>
#include <thread>
#include <atomic>
using namespace std::chrono;
std::atomic<double> pi;
void reportTime(const char* msg, steady_clock::duration span) {
auto ms = duration_cast<milliseconds>(span);
std::cout << msg << " - took - " <<
ms.count() << " milliseconds" << std::endl;
}
void run(int ID, double stepSize, int nthrds, int n)
{
double x;
double sum = 0.0;
for (int i = ID; i < n; i = i + nthrds){
x = (i + 0.5)*stepSize;
sum += 4.0 / (1.0 + x*x);
}
sum = sum * stepSize;
pi = pi + sum;
}
int main(int argc, char** argv) {
if (argc != 3) {
std::cerr << argv[0] << ": invalid number of arguments\n";
return 1;
}
int n = atoi(argv[1]);
int numThreads = atoi(argv[2]);
steady_clock::time_point ts, te;
// calculate pi by integrating the area under 1/(1 + x^2) in n steps
ts = steady_clock::now();
std::vector<std::thread> threads(numThreads);
double stepSize = 1.0 / (double)n;
for (int ID = 0; ID < numThreads; ID++) {
int nthrds = std::thread::hardware_concurrency();
if (ID == 0) numThreads = nthrds;
threads[ID] = std::thread(run, ID, stepSize, 8, n);
}
te = steady_clock::now();
for (int i = 0; i < numThreads; i++){
threads[i].join();
}
std::cout << "n = " << n << std::fixed << std::setprecision(15) << "\n pi(exact) = " << 3.141592653589793 << "\n pi(calcd) = " << pi << std::endl;
reportTime("Integration", te - ts);
// terminate
char c;
std::cout << "Press Enter key to exit ... ";
std::cin.get(c);
}
Question & Awnser
Can one safely use C++11 multi-threading as well as OpenMP in one and the same program but without interleaving them (i.e. no OpenMP statement in any code passed to C++11 concurrent features and no C++11 concurrency in threads spawned by OpenMP)?
On some platforms efficient implementation could only be achieved if the OpenMP run-time is the
only one in control of the process threads. Also there are certain aspects of OpenMP that might
not play well with other threading constructs, for example the limit on the number of threads set
by OMP_THREAD_LIMIT when forking two or more concurrent parallel regions.Since the OpenMP standard
itself does not strictly forbid using other threading paradigms, but neither standardises the
interoperability with such, supporting such functionality is up to the implementers. This means
that some implementations might provide safe concurrent execution of top-level OpenMP regions,
some might not. The x86 implementers pledge to supporting it, may be because most of them are
also proponents of other execution models (e.g. Intel with Cilk and TBB, GCC with C++11, etc.)
and x86 is usually considered an "experimental" platform (other vendors are usually much more conservative).
OpenMP code
//Workshop 3 using the scan and reduce with openMp
template <typename T, typename R, typename C, typename S>
int scan(
const T* in, // source data
T* out, // output data
int size, // size of source, output data sets
R reduce, // reduction expression
C combine, // combine expression
S scan_fn, // scan function (exclusive or inclusive)
T initial // initial value
)
{
/* int tile size = (n - 1)/ntiles + 1;
reduced[tid] = reduce(in + tid * tilesize,itile == last_tile ? last_tile_size : tile_size, combine, T(0));
#pragma omp barrier
#pragma omp single */
int nthreads = 1;
if (size > 0) {
// requested number of tiles
int max_threads = omp_get_max_threads();
T* reduced = new T[max_threads];
T* scanRes = new T[max_threads];
#pragma omp parallel
{
int ntiles = omp_get_num_threads(); // Number of tiles
int itile = omp_get_thread_num();
int tile_size = (size - 1) / ntiles + 1;
int last_tile = ntiles - 1;
int last_tile_size = size - last_tile * tile_size;
if (itile == 0)
nthreads = ntiles;
// step 1 - reduce each tile separately
for (int itile = 0; itile < ntiles; itile++)
reduced[itile] = reduce(in + itile * tile_size,
itile == last_tile ? last_tile_size : tile_size, combine, T(0));
// step 2 - perform exclusive scan on all tiles using reduction outputs
// store results in scanRes[]
excl_scan(reduced, scanRes, ntiles, combine, T(0));
// step 3 - scan each tile separately using scanRes[]
for (int itile = 0; itile < ntiles; itile++)
scan_fn(in + itile * tile_size, out + itile * tile_size,
itile == last_tile ? last_tile_size : tile_size, combine,
scanRes[itile]);
}
delete[] reduced;
delete[] scanRes;
}
return nthreads;
}
C++11 code
#include <iostream>
#include <omp.h>
#include <chrono>
#include <vector>
#include <thread>
using namespace std;
void doNothing() {}
int run(int algorithmToRun)
{
auto startTime = std::chrono::system_clock::now();
for(int j=1; j<100000; ++j)
{
if(algorithmToRun == 1)
{
vector<thread> threads;
for(int i=0; i<16; i++)
{
threads.push_back(thread(doNothing));
}
for(auto& thread : threads) thread.join();
}
else if(algorithmToRun == 2)
{
#pragma omp parallel for num_threads(16)
for(unsigned i=0; i<16; i++)
{
doNothing();
}
}
}
auto endTime = std::chrono::system_clock::now();
std::chrono::duration<double> elapsed_seconds = endTime - startTime;
return elapsed_seconds.count();
}
int main()
{
int cppt = run(1);
int ompt = run(2);
cout<<cppt<<endl;
cout<<ompt<<endl;
return 0;
}
