CppCon 2019: David Olsen “Faster Code Through Parallelism on CPUs and GPUs”

CppCon.org
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Discussion & Comments: / cpp
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Presentation Slides, PDFs, Source Code and other presenter materials are available at: github.com/Cpp...
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Ever since multicore CPUs became widely available, programmers have been working to get compute-intensive code to run in parallel and take advantage of CPU hardware parallelism. This effort has continued in the era of general-purpose programming on GPUs. There are many approaches to parallelizing C++ code on multicore CPUs or GPUs. C++11 threads, OpenMP or OpenACC pragmas, CUDA, and class libraries like Kokkos are among the options. The C++17 standard introduced parallel versions of standard algorithms, offering an approach that is fully portable across C++17 implementations and supports both CPUs and GPUs. This talk will survey many of these approaches and compare them for ease of use, clarity of the code, and performance. It will include an overview of the current state of implementations of C++17 parallel algorithms in different compilers.
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David Olsen
Software engineer, NVIDIA
David Olsen has more than two decades of software development experience in a variety of programming languages and development environments. For the last three years he has been the lead engineer for the PGI C++ compiler at NVIDIA. He is a member of the ISO C++ committee.
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Videos Filmed & Edited by Bash Films: www.BashFilms.com
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[@HarlowBAshur]

Yes, C++17 results should have had a standing ovation.

[@singinanarchist0]

At around 30:26, where are the applause ???

[@pascalmorf5753]

Thank you very much for this interesting comparison of parallelism implementations. I didn't find the source code of the different implementations of the salesman traveling on the GitHub link. Thanks

[@LeDabe]

What about openmp gpu offloading ?

[@sakuranooka]

Awesome way to walk through permutations!!!

[@cheleon94]

Very interesting talk!