CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA for general computing on its own GPUs (Graphics Processing Units). It empowers application developers to leverage the parallel processing capabilities of NVIDIA's GPUs to accelerate computation-heavy tasks, such as matrix operations, physics simulations, deep learning training, and real-time video processing. CUDA provides a C-like programming language that allows developers to write kernel functions, which are executed on the GPU, and manage memory between the host (CPU) and device (GPU) environments. Utilizing CUDA can lead to significant performance improvements in suitable applications, and it integrates well with various programming environments, including Python through libraries like PyCUDA or through frameworks like TensorFlow with GPU support. Understanding basic concepts such as kernels, threads, blocks, and warps is essential for developers to effectively harness the power of GPU programming with CUDA.
View the most prominent open source CUDA projects in the list below. Click on a specific project to view its alternative or complementary packages. Make comparisons and find the best package for your app.
- A high-throughput and memory-efficient inference and serving engine for LLMs☆47,581Updated this week
- World's fastest and most advanced password recovery utility☆22,677Updated this week
- Build and run Docker containers leveraging NVIDIA GPUs☆17,375Updated last year
- Instant neural graphics primitives: lightning fast NeRF and more☆16,588Updated 3 months ago
- kaldi-asr/kaldi is the official location of the Kaldi project.☆14,857Updated 3 weeks ago
- SGLang is a fast serving framework for large language models and vision language models.☆14,548Updated this week
- Open3D: A Modern Library for 3D Data Processing☆12,314Updated 2 weeks ago
- CUDA on non-NVIDIA GPUs☆11,349Updated this week
- Burn is a next generation Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.☆11,168Updated this week
- Solve puzzles. Learn CUDA.☆11,009Updated 8 months ago
- NumPy aware dynamic Python compiler using LLVM☆10,425Updated last week
- NumPy & SciPy for GPU☆10,208Updated 3 weeks ago
- cuDF - GPU DataFrame Library☆8,929Updated this week
- Containers for machine learning☆8,611Updated this week
- OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.☆8,503Updated last week
- A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other ma…☆8,397Updated this week
- Modular ZK(Zero Knowledge) backend accelerated by GPU☆7,765Updated 5 months ago
- CUDA Templates for Linear Algebra Subroutines☆7,540Updated last week
- Samples for CUDA Developers which demonstrates features in CUDA Toolkit☆7,475Updated this week
- Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.☆7,050Updated last week
- A flexible framework of neural networks for deep learning☆5,906Updated last year
- An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.☆5,537Updated last week
- ALIEN is a CUDA-powered artificial life simulation program.☆5,155Updated this week
- A Python framework for accelerated simulation, data generation and spatial computing.☆5,107Updated this week
- [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl☆4,962Updated last year
- A PyTorch Library for Accelerating 3D Deep Learning Research☆4,757Updated this week
- cuML - RAPIDS Machine Learning Library☆4,705Updated this week
- ArrayFire: a general purpose GPU library.☆4,704Updated this week
- Tengine is a lite, high performance, modular inference engine for embedded device☆4,467Updated 2 months ago
- Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.☆4,359Updated 3 weeks ago
- 📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA/Tensor Cores Kernels, HGEMM, FA-2 MMA etc.🔥☆4,385Updated this week
- Lightning fast C++/CUDA neural network framework☆4,022Updated 3 weeks ago
- HIP: C++ Heterogeneous-Compute Interface for Portability☆4,015Updated this week
- Making it easier to work with shaders☆4,023Updated this week
- Fast inference engine for Transformer models☆3,806Updated last month
- LightSeq: A High Performance Library for Sequence Processing and Generation☆3,274Updated 2 years ago
- Single C file, Realtime CPU/GPU Profiler with Remote Web Viewer☆3,214Updated 8 months ago
- Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.☆3,157Updated last week
- A retargetable MLIR-based machine learning compiler and runtime toolkit.☆3,139Updated this week
- A GPU-powered real-time analytics storage and query engine.☆3,050Updated 10 months ago