Gpu Computing
Showing 36 of 37 projects
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
A Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and machine learning.
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
An array framework for machine learning on Apple silicon with unified memory and dynamic graph construction.
A minimalist, high-performance machine learning framework for Rust with a focus on serverless inference and GPU support.
A Rust-based deep learning framework and tensor library optimized for flexibility, efficiency, and cross-platform portability.
A NumPy/SciPy-compatible array library for GPU-accelerated computing with Python, supporting NVIDIA CUDA and AMD ROCm.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
An open-source application that stabilizes video using gyroscope and accelerometer data from cameras or external devices.
A library for building and evaluating mathematical expressions and neural networks in Go, with automatic differentiation and GPU support.
An open-source machine learning framework for building classical, deep, or hybrid ML applications with a focus on performance and portability.
Rust language bindings for TensorFlow, providing idiomatic access to machine learning capabilities.
Specification for the WebGPU API and WGSL shading language, enabling high-performance GPU access for web applications.
A high-performance TensorFlow library for quantitative finance, providing mathematical methods, pricing models, and calibration tools.
A suite of GPU-accelerated machine learning algorithms with scikit-learn compatible APIs for 10-50x faster performance on large datasets.
A general-purpose tensor library for parallel computing across CPUs, GPUs, and hardware accelerators.
Enables distributed TensorFlow training and inferencing on Apache Spark and Hadoop clusters with minimal code changes.
A Python library for probabilistic modeling built on PyTorch, offering modular distributions, GPU support, and flexible model composition.
A neural network library optimized for dynamic structures that change per training instance, with C++ and Python bindings.
A JAX-based neural network library that provides a simple, object-oriented programming model for building and training models.
A .NET library to run C# code in parallel on the GPU through DX12 and D2D1, generating HLSL shaders dynamically.
An open-source library for high-performance subdivision surface evaluation on CPU and GPU, matching Pixar's Renderman precision.
Multi-dimensional arrays (tensors) and numerical definitions for Elixir, enabling machine learning and scientific computing.
Docker image providing the Python environment used by Kaggle Notebooks for data science competitions.
A benchmarking suite comparing the performance of public convolutional neural network implementations across multiple deep learning frameworks.
An open source container platform designed for simplicity, speed, and security in HPC and shared computing environments.
A collection of 45 OpenGL 3.x and 4.x examples with GLSL shaders demonstrating modern graphics programming techniques.
A C++ programming model for writing performance-portable applications targeting all major HPC platforms.
A Go library that simplifies TensorFlow's Go bindings with method chaining, automatic scoping, and type conversion.
A JAX-based library providing numerical differential equation solvers for ODEs, SDEs, and CDEs with autodifferentiation and GPU support.
A curated list of resources, libraries, tools, and demos for the WebGPU ecosystem.
A header-only C++ library for CUDA providing accelerated primitives for solving irregularly parallel problems on GPUs.
A high-performance GPU-accelerated Fast Fourier Transform library supporting Vulkan, CUDA, HIP, OpenCL, Level Zero, and Metal backends.
A JIT compiler for writing high-performance GPU programs in .NET languages like C#, offering CUDA-level performance with C# convenience.
A Python library for simulating spiking neural networks (SNNs) using PyTorch, geared towards biologically inspired machine learning.
A C++ GPU computing library providing an STL-like interface for OpenCL-based parallel programming.
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