sleef
bmas
| sleef | bmas | |
|---|---|---|
| 17 | 1 | |
| 590 | 4 | |
| - | - | |
| 8.1 | 4.8 | |
| 9 days ago | 4 months ago | |
| C | C | |
| Boost Software License 1.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sleef
-
The Case of the Missing SIMD Code
I'm the main author of Highway, so I have some opinions :D Number of operations/platforms supported are important criteria.
A hopefully unbiased commentary:
Simde allows you to take existing nonportable intrinsics and get them to run on another platform. This is useful when you have a bunch of existing code and tight deadlines. The downside is less than optimal performance - a portable abstraction can be more efficient than forcing one platform to exactly match the semantics of another. Although a ton of effort has gone into Simde, sometimes it also resorts to autovectorization which may or may not work.
Eigen and SLEEF are mostly math-focused projects that also have a portability layer. SLEEF is designed for C and thus has type suffixes which are rather verbose, see https://github.com/shibatch/sleef/blob/master/src/libm/sleef... But it offers a complete (more so than Highway's) libm.
-
Does anyone have any interest in my deep-learning framework?
But the other part about SIMD: I'm unsure if mgl-mat uses SIMD for transcendental functions or even for something like element-wise multiplication and division*. SIMD easily provides a speed-boost of 4-8 times which numpy uses. Libraries like sleef have been put to use by many.
- `constexpr` what?
- Advice on porting glibc trig functions to SIMD
-
SIMD intrinsics and the possibility of a standard library solution
Highway and Agner's VectorClass also have math functions. And SLEEF should definitely be mentioned.
-
Portable SIMD library
"SIMD Library for Evaluating Elementary Functions, vectorized libm and DFT" - https://github.com/shibatch/sleef
- SIMD Library for Evaluating Elementary Functions, Vectorized Libm and DFT
-
C library for multiple-precision floating-point arithmetic with correct rounding
Not mentioned in the list of users is SLEEF (https://github.com/shibatch/sleef), which provides fast approximations for various elementary functions. (It generates coefficients for the approximations with mpfr)
SLEEF itself is used by PyTorch.
-
How to speed up array writes?
If you are looking at floats, there's https://sleef.org
-
Benchmarking sine approximations and interpolators.
It would be interesting to see SLEEF added in the benchmarks.
bmas
-
How to speed up array writes?
(Shameless plug) I recently added AVX2 256-bit simd based integer copy instructions to BMAS - and this is accessible through bmas (extremely thankful to C2FFI!) And I'm wanting to try memcpy to see if that gets any further benefit, hoping to try some time during the week.
What are some alternatives?
nsimd - Agenium Scale vectorization library for CPUs and GPUs
sb-simd - A convenient SIMD interface for SBCL.
yenten-arm-miner-yespowerr16 - ARM 64 CPU miner for Yespower variant algorithms
cl-bmas - C2FFI generated bindings for BMAS
vector-libm
crlibm - A mirror of the CRLibm project from INRIA Forge
xbyak_aarch64
rlibm-32 - RLibm for 32-bit representations (float and posit32)
FftSharp - A .NET Standard library for computing the Fast Fourier Transform (FFT) of real or complex data
streamvbyte - Fast integer compression in C using the StreamVByte codec
highway - Performance-portable, length-agnostic SIMD with runtime dispatch