Performance-portable, length-agnostic SIMD with runtime dispatch
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Updated
Jun 28, 2024 - C++
Performance-portable, length-agnostic SIMD with runtime dispatch
Chromium fork named after radioactive element No. 90. Windows and MacOS/Raspi/Android/Special builds are in different repositories, links are towards the top of the README.md.
Firefox fork with compiler optimizations and patches from Librewolf, Waterfox, and GNU IceCat.
Windows 7 builds of Mercury Browser (Based on ESR115 rather than stable tip-of-tree)
flat assembler g - adaptable assembly engine
The hyper-hackable text editor - Compiler Optimized, Community Maintained Fork
Repo to serve AVX2 Linux builds of Thorium. https://github.com/Alex313031/Thorium/
One header file library that implement missing transcendental math functions (cos, sin, acos, and more....) using 100% AVX/Neon instructions (no branching)
Repo to serve AVX2 Windows builds of Thorium. https://github.com/Alex313031/Thorium/
an exercise in SIMD-optimization
Document Level Sentiment Analysis is an End-to-End deep learning workflow using Hugging Face transformers API to do a "classification" task at document level, to analyze the sentiment of input document containing English sentences or paragraphs.
AVX-512 documentation beyond what Intel provides
Accelerate SHA256 computations in pure Go using AVX512, SHA Extensions for x86 and ARM64 for ARM. On AVX512 it provides an up to 8x improvement (over 3 GB/s per core). SHA Extensions give a performance boost of close to 4x over native.
AVX2 and SSE2 usecases and benchmarks
Information about AVX-512 support on recent Intel processors
rewrite CON_INT function to build SIMD-version convolution
Implementation of 2D Convolution operation for Neural Networks using Intel x86(i368)/x86-6(amd64) AVX-256 instructions. All data flow methods, i.e input stationary, weight stationary and output stationary are implemented. The forward pass of Alexnet architecture is constructed using it.
Simple and performance neural network .NET library with AVX acceleration
Convolutional Neural Network Optimization using Intel AVX and OpenMP
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