cuhnsw
DOKSparse
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cuhnsw | DOKSparse | |
---|---|---|
4 | 2 | |
112 | 2 | |
- | - | |
0.0 | 4.2 | |
about 3 years ago | 10 months ago | |
Cuda | Cuda | |
Apache License 2.0 | MIT License |
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cuhnsw
- [P] CUHNSW - CUDA Implementation of Hierarchical Navigable Small World (HNSW) Graph algorithm for Approximate Nearest Neighbor Search
- [P] CUHNSW - CUDA Implementation of Hierarchical Navigable Small World (HNSW) Graph Algorithm for Approximate Nearest Neighbor Search
- CUHNSW - CUDA implementation of HNSW for Approximate Nearest Neighbor
- CUHNSW - CUDA implementation of Hierarchical Navigable Small World (HNSW) graph algorithm for Approximate Nearest Neighbor (ANN)
DOKSparse
- GDlog: A GPU-Accelerated Deductive Engine
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tensor.to_sparse() Memory Allocation
If using sparse tensors is a must, you can look into DOK sparse format, which is supported for 2d matrices in scipy. it kinda allows you to access any element of the sparse tensor in constant time, which makes it possible to create your tensor directly in sparse format, skipping the need to create a dense numpy array first. In case you need a GPU version of this, I have a library that implements sparse dok tensor in pytorch and cuda. currently it's GPU only.
What are some alternatives?
Open3D - Open3D: A Modern Library for 3D Data Processing
CUDA-Guide - CUDA Guide
lightseq - LightSeq: A High Performance Library for Sequence Processing and Generation
TorchPQ - Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda
cuml - cuML - RAPIDS Machine Learning Library
cub - [ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl
FirstCollisionTimestepRarefiedGasSimulator - This simulator computes all possible intersections for a very small timestep for a particle model
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
MegBA - MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
cccl - CUDA C++ Core Libraries
warpcore - A Library for fast Hash Tables on GPUs
gdlog