std-simd VS ispc

neither proposing nor taking a position on this possible addition)

> ... For completeness we would also like to add that a serious issue is that C still lacks vector operations.

Those are good points. The authors don't take a stance on it, but I do think that syntax for packed structs should be standardized. IMO, so should syntax for inline assembly (both as optional features). These are already common extensions; this is exactly what they should standardize. The additions of "typeof" and #embed are also good examples of this (they had been talking about adding #embed since 1995 [1]).

As for vector instructions, I'm unsure how it could be implemented in a standard way, but I'm not against it. Maybe something like this [2], but with the syntax changed for C instead of C++.

[1]: https://groups.google.com/g/comp.std.c/c/zWFEXDvyTwM

[2]: https://github.com/VcDevel/std-simd

Interesting, thanks for sharing :)

At the time we open-sourced Highway, the standardization process had already started and there were some discussions.

I'm curious why stdlib is the only path you see to default? Compare the activity level of https://github.com/VcDevel/std-simd vs https://github.com/google/highway. As to open-source usage, after years of std::experimental, I see <200 search hits [1], vs >400 for Highway [2], even after excluding several library users.

But that aside, I'm not convinced standardization is the best path for a SIMD library. We and external users extend Highway on a weekly basis as new use cases arise. What if we deferred those changes to 3-monthly meetings, or had to wait for one meeting per WD, CD, (FCD), DIS, (FDIS) stage before it's standardized? Standardization seems more useful for rarely-changing things.

1: https://sourcegraph.com/search?q=context:global+std::experim...

2: https://sourcegraph.com/search?q=context:global+HWY_NAMESPAC...

std-simd - 451 GH stars

Bad news. For SIMD there are not cross-platform intrinsics. Intel intrinsics map directly to SSE/AVX instructions and ARM intrinsics map directly to NEON instructions.

For cross-platform, your best bet is probably https://github.com/VcDevel/std-simd

There's https://eigen.tuxfamily.org/index.php?title=Main_Page But, it's tremendously complicated for anything other than large-scale linear algebra.

And, there's https://github.com/microsoft/DirectXMath But, it has obvious biases :P

But after I worked with auto-vectorization(I wrote about that in part 2), I decided to switch and try myself by adding intrinsics if I was able. You can track my progress here:https://github.com/VcDevel/std-simd/pull/35

STD-SIMD it's almost the same project I was working, but a bit advance https://github.com/VcDevel/std-simd.

If you are worried about Intel vs Arm vs whatever, use https://github.com/VcDevel/std-simd

or https://github.com/KhronosGroup/Vulkan-Hpp which help quite a bit. Or https://github.com/VcDevel/std-simd.

If you want GUIs, same, you have at least (but not only) Qt or WxWidgets.

Want to interface scripting? Pybind11, Boost.Python, WrenBind17 for Wren, Sol2 for Lua... and all things that interface to C work also if you feel brave...

I really think that when it is about getting the job done... C++ goes a long way towards the task.

This is my 20 year experience of C++, almost 13 of those years professionally. Now, back to read the paper. :)

... I just checked your link and wouldn't say that any of these languages have SIMD more than C++ has it currently -

- Java: incubation stage (how is that different from https://github.com/VcDevel/std-simd). Also Java is only getting it soonish for... amd64 and aarch64 ??

This so-called GPU programming model has existed many decades before the appearance of the first GPUs, but at that time the compilers were not so good like the CUDA compilers, so the burden for a programmer was greater.

As another poster has already mentioned, there exists a compiler for CPUs which has been inspired by CUDA and which has been available for many years: ISPC (Implicit SPMD Program Compiler), at https://github.com/ispc/ispc .

NVIDIA has the very annoying habit of using a lot of terms that are different from those that have been previously used in computer science for decades. The worst is that NVIDIA has not invented new words, but they have frequently reused words that have been widely used with other meanings.

SIMT (Single-Instruction Multiple Thread) is not the worst term coined by NVIDIA, but there was no need for yet another acronym. For instance they could have used SPMD (Single Program, Multiple Data Stream), which dates from 1988, two decades before CUDA.

Moreover, SIMT is the same thing that was called "array of processes" by C.A.R. Hoare in August 1978 (in "Communicating Sequential Processes"), or "replicated parallel" by Occam in 1985 or "PARALLEL DO" by "OpenMP Fortran" in 1997-10 or "parallel for" by "OpenMP C and C++" in 1998-10.

The only (but extremely important) innovation brought by CUDA is that the compiler is smart enough so that the programmer does not need to know the structure of the processor, i.e. how many cores it has and how many SIMD lanes has each core. The CUDA compiler distributes automatically the work over the available SIMD lanes and available cores and in most cases the programmer does not care whether two executions of the function that must be executed for each data item are done on two different cores or on two different SIMD lanes of the same core.

ISPC: https://github.com/ispc/ispc

Have you looked at [ISPC - Intel SPMD Program Compiler][0]?

  [0]: https://github.com/ispc/ispc