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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.
gdlog
What are some alternatives?
cub - [ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl
treeedb - Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
MegBA - MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
cccl - CUDA C++ Core Libraries
CUDA-Guide - CUDA Guide
NMT4RDFS - Neural Machine Translation for RDFS reasoning: code and datasets for "Deep learning for noise-tolerant RDFS reasoning" http://www.semantic-web-journal.net/content/deep-learning-noise-tolerant-rdfs-reasoning-4
cuhnsw - CUDA implementation of Hierarchical Navigable Small World Graph algorithm
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
TorchPQ - Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda
FuXi - Chimezie Ogbuji's FuXi reasoner. NON-FUNCTIONING, RETAINED FOR ARCHIVAL PURPOSES. For working code plus version and associated support requirements see:
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
warpcore - A Library for fast Hash Tables on GPUs