About the internal implementation: The internal implementation need* threads which permit reuse the initialized variables*. This feature is no implemented in the C++ standard at today, and I didn’t see in the thread pool implementations which I examined I implemented this with threads and atomic variables. The thread are created at the beginning of the program where initialize the thread pool variables which are destroyed with the thread.* The threads are created only 1 time*. The parallel_for and parallel_while are implemented with atomic variables in a short and easy way to understand and debug. The works for to execute by the threads are implemented by std::function objects stored in a concurrent stack. The work time estimated by each object function is a few milliseconds. This internal design is highly efficient with many threads. ( I checked in a dual socket servers with 32 and 48 threads) and the results compared with GCC parallel sort are better with many threads. About the goals of this library : The first is to create a library* extremely easy to understand and use*. (You only need to include boost/sort/parallel/sort.hpp ) The library must be *independent of any other code*.( You can use separately, only need to copy the boost/sort/parallel folder, and all the code needed is included) The library must use *only the C++11 compliant compiler*. The code can run in an embedded multi core system or in a powerful processor, plenty of cores, in a big server. The advantage of the algorithms are *parallel_sort, sample_sort* : this algorithm is commonly accepted as the fastest for the parallel stable sort. The implementation is *notoriously faster than the provided by the GCC compiler* ( based on Open MP ) using less memory. TBB don’t provide parallel stable sort, and only have an experimental code in their web pages *parallel_sort* : the advantage of this algorithm, invented by me, is the memory usage.* The speed is similar to GCC parallel sort, but THE MEMORY USED IS A HALF.* The algorithms are *exception safe*, meaning that the exceptions generated by the algorithms guarantee the integrity of the objects to sort, but not their relative order. If the exception is generated inside the objects (in the move or in the copy constructor) the results can be unpredictable Thanks by your interest Francisco * I take a quick look at your library, and see many interesting things inside. 2016-11-20 21:24 GMT+01:00 Christophe Henry :

Hi Boost Community,

I did not manage yet to write a full review. One week is a bit short so a few more days will be necessary. I provide some performance tests first.

To check the library performance, I pitted it against my own versions of a parallel mergesort and quicksort provided by the asynchronous library ( https://github.com/henry-ch/asynchronous), which is now ready for review, so we have a stable state for testing. I also added a parallel version inside the asynchronous library using Francisco's single-thread implementations to compare with his parallel ones.

I tested on a i7-5960X. Due to time limits, I could not test on more interesting NUMA platforms (I did not get the reviewed library to compile for my KNC) so the tests do not pretend to have value outside the i7 architecture. I linked with tbb_malloc_proxy to limit memory influence.

In a nutshell. I'm not a big fan of the parallel version of the algorithms. It seems to be based on std::async, so that a lot of threads are started and joined at every call. I would suggest using a threadpool. OTOH the single-threaded ones are interesting, especially the stable_sort and intro_sort for cases where usage of spreadsort is not possible.

Cheers, Christophe

A short summary:

100000000 uint64_t elements already sorted: OMP parallel sort : 0.390899 secs Boost parallel sort : 0.064965 secs OMP parallel stable sort : 1.06128 secs Boost sample sort : 0.0695357 secs Boost parallel stable sort : 0.0662401 secs

Asynchronous parallel sort : 0.0167134 secs (same with other algorithms)

Asynchronous provides a special optimization for this case.

I added this one: 100000000 uint64_t elements reverse sorted

OMP parallel sort : 0.317039 secs Boost parallel sort : 0.581381 secs

OMP parallel stable sort : 1.06448 secs Boost sample sort : 0.524746 secs Boost parallel stable sort : 0.73036 secs

Asynchronous parallel sort : 0.0478701 secs

Asynchronous provides a special optimization for this case. I this the library should do it too. This one is pretty common and a popular DOS attack.

100000000 uint64_t elements randomly filled OMP parallel sort : 1.03594 secs Boost parallel sort : 0.796447 secs

OMP parallel stable sort : 1.28601 secs Boost sample sort : 0.818954 secs Boost parallel stable sort : 1.13604 secs

Asynchronous parallel quickspreadsort: 0.587432 secs Asynchronous quick_intro_sort : 0.728393 secs

This mixing of quicksort degrading into a spreadsort works here best. The parallel adaptation of intro_sort is not bad either and best of other library algorithms.

Asynchronous parallel stable sort : 1.26141 secs Asynchronous boost::stable sort : 0.804814 secs

Interesting. The stable version of the library easily beats std::stable_sort I used until now.

10000000 strings randomly filled

OMP parallel sort : 1.05803 secs Boost parallel sort : 0.933055 secs

OMP parallel stable sort : 1.12269 secs Boost sample sort : 0.889216 secs Boost parallel stable sort : 1.56564 secs

Asynchronous parallel quickspreadsort: 0.788856 secs Asynchronous quick_intro_sort : 0.893652 secs

Asynchronous parallel stable sort : 1.23495 secs Asynchronous boost::stable sort : 1.21817 secs

Similar results.

Let's move on to big objects:

1562500 elements of size 512 randomly filled H E A V Y C O M P A R I S O N

OMP parallel sort : 0.308923 secs Boost parallel sort : 0.342992 secs

Asynchronous parallel_quicksort : 0.246709 secs Asynchronous quick_intro_sort : 0.269666 secs

Both quicksorts are best, with a slight advantage to intro_sort.

The light comparison is similar.

My test code (the modified benchmark.cpp provided by the library): https://github.com/henry-ch/asynchronous/blob/master/libs/ asynchronous/test/perf/boost_sort/benchmark_sort.cpp

The full test results: https://github.com/henry-ch/asynchronous/blob/master/libs/ asynchronous/test/perf/boost_sort/Results_sort.txt

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