arXiv (All)
open access
SpSYRK: Half the Work in Distributed Sparse Matrix Multiplication
The symmetric rank-$k$ update (SYRK), $C = AA^\top$, computes the dot product of each pair of rows of $A$, producing the Gram matrix $C$. Its sparse variant underpins similarity search in machine learning, graph analytics, and genomics, including Jaccard similarity on datasets too large for a single
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- MatrixFSDP: communication-free matrix optimizers under ZeRO-3 parameter sharding#926111
- Wasserstein mixing of a systematic-scan random rotation sampler#1160766
- More Asymmetry Yields Faster Matrix Multiplication#635535
- More Asymmetry Yields Faster Matrix Multiplication#633186
- Convergence of Rotation-based Matrix Optimizers: A Unified Analysis of SOAP, Conda, and SPlus#1050693
Record · ID 1129358
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive