CUDA integration for Python, plus shiny features
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Updated
Jul 15, 2024 - Python
CUDA integration for Python, plus shiny features
a python package for gravitational wave analysis with the F-statistic
Implementation of the Apriori and Eclat algorithms, two of the best-known basic algorithms for mining frequent item sets in a set of transactions, implementation in Python.
This container is no longer supported, and has been deprecated in favor of: https://github.com/joehoeller/NVIDIA-GPU-Tensor-Core-Accelerator-PyTorch-OpenCV
Algorithms implemented in CUDA + resources about GPGPU
Audio Fingerprinting and Recognition in Python using NVidia's CUDA
Parallel Processing Teaching Toolkit
PNG grayscale and blur filters using PyCUDA
Use CUDA for eBeam Lithography Simulation
Bacteria Detection based on YOLOv5 for Jetson Nano.
Numpy and pyCUDA implementation of subKmeans
Brain tumor (low-grade and high-grade glioma) segmentation using unsupervised methods
pyCUDA implementation of forward propagation for Convolutional Neural Networks
GPU Accelerated Image Filters
Dijkstra algorithm has been implemented using CUDA technology in parallel and serial ways to measure execution time for both versions. It serves as a resource for the college community to understand and apply the algorithm better.
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