DaCe - Data Centric Parallel Programming
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Updated
Jul 6, 2024 - Python
CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
DaCe - Data Centric Parallel Programming
A high-throughput and memory-efficient inference and serving engine for LLMs
Main repository for QMCPACK, an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support
Open Voice OS Status Page
Comprehensive, GPU accelerated framework for developing universal virtual quantum processors
NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes
✨ Zero-code distributed tracing and profiling, observability via eBPF 🚀
OneSweep, implemented in CUDA, D3D12, and Unity style compute shaders. Theoretically portable to all wave/warp/subgroup sizes.
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
An efficient C++17 GPU numerical computing library with Python-like syntax
FlashInfer: Kernel Library for LLM Serving
A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data.
(in progress) SAH kd-tree parallel construction algorithm implementation
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Implementations of various simulations for integrate and fire models, as well as conductance based models with synaptic neurotransmission
Created by Nvidia
Released June 23, 2007