fastbase64
nsimd
| fastbase64 | nsimd | |
|---|---|---|
| 1 | 2 | |
| 420 | 315 | |
| - | 1.6% | |
| 3.5 | 0.0 | |
| about 1 month ago | over 2 years ago | |
| C | C | |
| GNU General Public License v3.0 or later | MIT License |
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fastbase64
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Designing a SIMD Algorithm from Scratch
How does this compare to fastbase64[0]? Great article, I'm happy to see this sort of thing online. I wish I could share the author's optimism about portable SIMD libraries.
[0]: https://github.com/lemire/fastbase64
nsimd
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SPO600 project part 1
I've decided to switch to something better, and after a few hours of searching, I found this repository: NSIMD https://github.com/agenium-scale/nsimd FastDifferentialCoding https://github.com/lemire/FastDifferentialCoding VS https://github.com/VcDevel/Vc XSIMD https://github.com/xtensor-stack/xsimd
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All C++20 core language features with examples
> - 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 use as https://github.com/taskflow/taskflow ?
- Debugging cross-platform code using couts, cerrs and printfs
because people never use console.log in JS or System.println in C# maybe ?
- Forced to use boost for even quite elementary operations on std::strings.
can you point to non-trivial java projects that do not use Apache Commons ? Also, the boost string algorithms are header-only so you will end up with exactly the same binaries that if it was in some std::string_algorithms namespace:
https://gcc.godbolt.org/z/43vKadbde
What are some alternatives?
highway - Performance-portable, length-agnostic SIMD with runtime dispatch
simde - Implementations of SIMD instruction sets for systems which don't natively support them.
TurboRLE - TurboRLE-Fastest Run Length Encoding
sleef - SIMD Library for Evaluating Elementary Functions, vectorized libm and DFT
std-simd - std::experimental::simd for GCC [ISO/IEC TS 19570:2018]
Vc - SIMD Vector Classes for C++
highway - Highway - A Modern Javascript Transitions Manager
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
Vcpkg - C++ Library Manager for Windows, Linux, and MacOS
tensorflow - An Open Source Machine Learning Framework for Everyone