Repository navigation
gpu-computing
- Website
- Wikipedia
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Video stabilization using gyroscope data
[ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
High-performance TensorFlow library for quantitative finance.
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. Free for non-commercial use.
Resource scheduling and cluster management for AI
General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
GPGPU microprocessor architecture
CUDA integration for Python, plus shiny features
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Deep learning in Rust, with shape checked tensors and neural networks
😎 Curated list of awesome things around WebGPU ecosystem.
The write-once-run-anywhere GPGPU library for Rust
Compiler for multiple programming models (SYCL, C++ standard parallelism, HIP/CUDA) for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
Simulation of spiking neural networks (SNNs) using PyTorch.
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
An efficient C++17 GPU numerical computing library with Python-like syntax
TornadoVM: A practical and efficient heterogeneous programming framework for managed languages