nerfstudio-project / gsplat
CUDA accelerated rasterization of gaussian splatting
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CUDA accelerated rasterization of gaussian splatting
DeepEP: an efficient expert-parallel communication library
Graphics Processing Units Molecular Dynamics
Causal depthwise conv1d in CUDA, with a PyTorch interface
DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling
RCCL Performance Benchmark Tests
CUDA Kernel Benchmarking Library
NCCL Tests
cuGraph - RAPIDS Graph Analytics Library
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.
[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.
[ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl
Tile primitives for speedy kernels
LLM training in simple, raw C/CUDA
Instant neural graphics primitives: lightning fast NeRF and more
cuVS - a library for vector search and clustering on the GPU
Fastest kernels written from scratch