Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Thrust | 4,843 | 1 | 3 months ago | 6 | April 06, 2022 | 183 | other | C++ | ||
[ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl | ||||||||||
Stdgpu | 990 | 8 months ago | 8 | apache-2.0 | C++ | |||||
stdgpu: Efficient STL-like Data Structures on the GPU | ||||||||||
Learn Gpgpu | 29 | 2 years ago | 1 | Cuda | ||||||
Algorithms implemented in CUDA + resources about GPGPU | ||||||||||
Cualgo | 24 | 5 months ago | mit | Python | ||||||
A cross-platform Pytnon library for fundamental algorithm with GPU-accelerated computing | ||||||||||
Dpcluster | 21 | 11 years ago | 1 | Python | ||||||
Efficient Dirichlet process clustering | ||||||||||
Openph | 14 | 3 years ago | apache-2.0 | Cuda | ||||||
Parallel reduction of boundary matrices for Persistent Homology with CUDA | ||||||||||
Gpu Toolkit | 14 | 4 months ago | mit | Cuda | ||||||
🦚 🧰 Collection of basic GPU algorithms implemented in CUDA C++. | ||||||||||
Gpu Cuda Self Organising Maps | 7 | a year ago | mit | C++ | ||||||
🧠 💡 📈 A project based in High Performance Computing. This project was built using CUDA (Compute Unified Device Architecture), C++ (C Plus Plus), C, CMake and JetBrains CLion. The scenario of the project was a GPU-based implementation of the Self-Organising-Maps (S.O.M.) algorithm for Artificial Neural Networks (A.N.N.), with the support of CUDA (Compute Unified Device Architecture), using its offered parallel optimisations and tunings. The final goal of the project was to test the several GPU-based implementations of the algorithm against a given CPU-based implementation of the same algorithm and, evaluate and compare the overall performance (speedup, efficiency and cost). |