Parallel Computing
Showing 36 of 186 projects
A lock-free, wait-free, continuation-stealing tasking library for C++20 built on coroutines, enabling ultra-fine-grained parallelism.
An R package for the quantitative analysis of textual data, providing comprehensive tools for natural language processing and text management.
An automated feature generation framework for tabular data that discovers expert-level features to boost machine learning model performance.
An efficient R package for text analysis and NLP with fast vectorization, topic modeling, and word embeddings.
A Python library for building high-performance, memory-efficient ensemble learning networks with a Scikit-learn compatible API.
Write native Ruby extensions in Rust to replace slow Ruby methods with high-performance Rust implementations.
Rust bindings for ArrayFire, a high-performance parallel computing library with support for CUDA, OpenCL, and CPU backends.
A pure Java machine learning library with no external dependencies, offering a wide collection of algorithms and parallel execution support.
A high-productivity C++ library for parallel programming across devices using Data Parallel C++ (DPC++) APIs.
An open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python.
A C++14 header-only library for high-performance video and image processing using meta-programming and SIMD optimizations.
A C++ vector expression template library for OpenCL, CUDA, and OpenMP that simplifies GPGPU development.
IPython-based environment for reproducible machine learning research with unified wrappers for multiple ML libraries.
A collection of functional programming libraries and utilities for F# development.
A portable, zero-overhead C++ header-only library implementing std::experimental::simd for explicit data-parallel programming.
A Rust crate providing efficient implementations of common machine learning algorithms with support for dense and sparse data.
A scalable C++ coroutine framework for highly parallelized CPU-bound and IO-bound workloads.
A header-only C++17 library for building and executing directed acyclic graphs of asynchronous tasks.
A Python package for visualizing and processing 2D/3D point clouds with interactive rendering and parallelized queries.
A collection of GPU-accelerated parallel game simulators for reinforcement learning, built with JAX.
An optimized distributed gradient boosting library for fast and accurate machine learning on large datasets.
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
A lightweight Python decision tree framework supporting ID3, C4.5, CART, CHAID, regression trees, gradient boosting, random forest, and AdaBoost with categorical feature support.
An extensible Rust framework for backend-agnostic, high-performance parallel computations on CUDA, OpenCL, and CPU.
A high-performance Rust framework for asynchronous task orchestration using Flow-Based Programming and directed acyclic graphs (DAGs).
A parallel deep learning framework written in modern Fortran for training and inference of dense, convolutional, and transformer networks.
A C++ task scheduling framework designed for game engines to express parallelism and maximize performance on multi-processor platforms.
A parallel gzip decompressor with fast random access, utilizing multi-core CPUs for high-speed decompression of standard gzip files.
A high-performance Rust library for graph algorithms, built on a parallel CSR data structure for large-scale graphs.
A Go library providing a structured concurrency API to simplify parallel task management, error aggregation, and panic propagation.
A V library for AI and high-performance scientific computing with pure-V BLAS/LAPACK implementations.
A V library for AI and high-performance scientific computing with pure-V BLAS/LAPACK implementations.
An exascale many-physics flow solver for compressible multi-phase simulations, scaling to 200 trillion grid points on 43K+ GPUs.
A C++ template library optimized for GPUs providing high-performance implementations of common algorithms like scan, reduce, transform, and sort.
A Go implementation of neural networks including BackPropagation, RBF, and Perceptron networks with parallel processing capabilities.
A hardware-accelerated Python library for running Quality-Diversity and neuroevolution algorithms in minutes instead of days.
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