Search Results for "simd"

Polars is a high-performance, multi-language DataFrame library built in Rust using Apache Arrow. It delivers blazing-fast, vectorized, and parallel data manipulation with both eager and lazy execution, making it an excellent tool for data processing in Python, Rust, Node.js, R, and SQL contexts.

...Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. It supports NHWC tensor layouts and allows flexible striding along the channel dimension to efficiently handle channel-split and concatenation operations without additional cost.

HighwayHash is a fast, keyed hash function intended for scenarios where you need strong, DoS-resistant hashing without the full overhead of a general-purpose cryptographic hash. It’s designed to defeat hash-flooding attacks by mixing input with wide SIMD operations and a branch-free inner loop, so adversaries can’t cheaply craft many colliding keys. The implementation targets multiple CPU families with vectorized code paths while keeping a portable fallback, yielding high throughput across platforms. It exposes simple one-shot and streaming APIs, so you can hash short keys or long byte streams with the same function. ...

libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, NEON, AltiVec) to accelerate baseline JPEG compression and decompression on x86, x86-64, ARM, and PowerPC systems. On such systems, libjpeg-turbo is generally 2-6x as fast as libjpeg, all else being equal. On other types of systems, libjpeg-turbo can still outperform libjpeg by a significant amount, by virtue of its highly-optimized Huffman coding routines.

Fastest Integer Compression. ALL functions are available for AMD/Intel, 64-bit ARMv8 NEON Linux+MacOS/M1 & Power9 Altivec. 100% C (C++ headers), as simple as memcpy. OS:Linux amd64, arm64, Power9, MacOs (Amd/intel + Apple M1).

...The source code does not depend on any other libraries. What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler. SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM. The model file has also been provided in directory ./models/. The file examples/detect-image.cpp and examples/detect-camera.cpp show how to use the library. The library was trained by libfacedetection.train. You can copy the files in directory src/ into your project, and compile them as the other files in your project. ...

...The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems.

SSE Scan Library (SSESL) provides functions to retrieve information about data taking advantage of the 128-bit SIMD operations present in any CPU supporting the MMX2/KNI.

A C++ header library for fast operations on vectors/matrices (3D/3x3) using Streaming SIMD Extensions (SSE, SSE2, SSE3, SSE4); Tends to be used in 3D graphics applications and game developement.