Vc

I'm surprised no one has mentioned Vc. I found ispc clunky and not as performant, and std::simd didn't support some useful math ops like rsqrt. Vc has been around for years, I have no trouble including it in my codes, it has masking and many of the most useful math ops, and I can get over 1 TF/s on a consumer-grade Ryzen and at least 3 TF/s on the big Epyc CPUs. https://github.com/VcDevel/Vc https://github.com/Applied-Scientific-Research/nvortexVc

> does not run on the GPU both Cuda and the Metal shader language are C++, so is OpenCL since 2.0 (https://www.khronos.org/opencl/), so is AMD ROCm's HIP (https://github.com/ROCm-Developer-Tools/HIP), so is SYCL (https://www.khronos.org/sycl/)? C++ is pretty much the language that runs most on GPUs. > no vector instructions, There's a thousand different possibilities for SIMD in C++, from #pragma omp simd, to libs such as std::experimental::simd (https://en.cppreference.com/w/cpp/experimental/simd/simd), Eve (https://github.com/jfalcou/eve), Highway (https://github.com/google/highway), Vc (https://github.com/VcDevel/Vc)...

Here is my source: https://github.com/VcDevel/std-simd Ah, but this repo mentions that the GCC 11 implementation apparently also works with clang: https://github.com/VcDevel/Vc. Thanks!

> - Waiting for Cross-Platform standardized SIMD vector datatypes which language has standardized SIMD vector datatypes ? most languages don't even have any ability to express SIMD while in C++ I can just use Vc (https://github.com/VcDevel/Vc), nsimd (https://github.com/agenium-scale/nsimd) or one of the other ton of alternatives, and have stuff that JustWorksTM on more architectures than most languages even support - Using nonstandard extensions, libraries or home-baked solutions to run computations in parallel on many cores or on different processors than the CPU what are the other native languages with a standardized memory model for atomics ? and, what's the problem with using libraries ? it's not like you're going to use C# or Java's built-in threadpools if you are doing any serious work, no ? Do they even have something as easy to u

The megahertz-scaling "Free Lunch" was declared dead 15 years ago [http://www.gotw.ca/publications/concurrency-ddj.htm] and it's been only getting deader. People are finally, grudgingly accepting that they must go parallel unless we want to see software performance stagnate permanently. For most people here, the issue has been obvious since before they learned to program. But, still they are putting off learning how to deal with it. The first, obvious answer to that is threading. But, in my experience, SIMD is a bigger bang for the buck for two reasons: 1) No synchronization problems. 2) Better cache utilization. It's not just that SIMD forces you to work in large, contiguous blocks. Fun fact: When you aren't using SIMD you are only using a fraction of your L1 cache bandwidth! A big challenge is that SIMD intrinsic-function APIs a

also worth mentioning here is Vc - portable, zero-overhead C++ types for explicitly data-parallel programming https://github.com/VcDevel/Vc/ (http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2017/p021...).