Abstract: Exploiting the numeric symmetry in sparse matrices to reduce their memory footprint is very tempting for optimizing the memory-bound Sparse Matrix-Vector Multiplication (SpMV) kernel.
Abstract: Sparse Matrix Vector multiplication (SpMV) is a fundamental operation in various computational science applications, characterized by a significant degree of inherent parallelism. Recent ...
You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.
This project implements Symmetric Non-negative Matrix Factorization (SymNMF) for clustering, combining Python and C for efficiency. It compares SymNMF with K-Means clustering on different datasets.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback