simd_decimal
ispc
simd_decimal | ispc | |
---|---|---|
1 | 4 | |
10 | 2,410 | |
- | 0.7% | |
10.0 | 9.5 | |
over 1 year ago | about 22 hours ago | |
Rust | C++ | |
MIT License | BSD 3-clause "New" or "Revised" License |
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simd_decimal
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SIMD intrinsics and the possibility of a standard library solution
Compare this neon parser and this sse parser, or for a very direct example what happens if you naively do the x86 method of vector search on arm. The shuffle and accumulation for each parser is drastically different, since the set of horizontal multiply-accumulates are different.
ispc
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Implementing a GPU's Programming Model on a CPU
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.
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SIMD intrinsics and the possibility of a standard library solution
ISPC: https://github.com/ispc/ispc
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Prefix Sum with SIMD
Have you looked at [ISPC - Intel SPMD Program Compiler][0]?
[0]: https://github.com/ispc/ispc
- Duff’s Device in 2021
What are some alternatives?
sleef - SIMD Library for Evaluating Elementary Functions, vectorized libm and DFT
highway - Performance-portable, length-agnostic SIMD with runtime dispatch
simde - Implementations of SIMD instruction sets for systems which don't natively support them.
Beef - Beef Programming Language
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
ParallelReductionsBenchmark - Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!
version2 - Vector class library, latest version
micro-profiler - Cross-platform low-footprint realtime C/C++ Profiler
elena-lang - ELENA is a general-purpose language with late binding. It is multi-paradigm, combining features of functional and object-oriented programming. Rich set of tools are provided to deal with message dispatching : multi-methods, message qualifying, generic message handlers, run-time interfaces
Skia - Skia is a complete 2D graphic library for drawing Text, Geometries, and Images.
lunix - Lua Unix Module.