Algebraic Geometric Rook Codes for Coded Distributed Computing

We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully general matrix multiplication and show that in this case we serendipitously have that all functions can be computed in a distributed fault-tolerant fashion over finite fields. This generalizes previous results on the topic. We prove that the associated codes achieve a recovery threshold similar to the ones for characteristic zero fields but now with a factor that is proportional to the genus of the underlying function field. In particular, we have that the recovery threshold of these codes is proportional to the classical complexity of matrix multiplication by a factor of at most the genus.

Authors

Institutions

Publication Details

Published
2024-11-24
DOI
https://doi.org/10.1109/itw61385.2024.10807041
Citations
4
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
1.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Algebraic Geometric Rook Codes for Coded Distributed Computing

Pedro Soto, Gretchen L. Matthews
4 citations
Advanced Data Storage Technologies
1.40
article

Algebraic Geometric Rook Codes for Coded Distributed Computing

Pedro Soto, Gretchen L. Matthews
article en
4 citations

Abstract

We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully general matrix multiplication and show that in this case we serendipitously have that all functions can be computed in a distributed fault-tolerant fashion over finite fields. This generalizes previous results on the topic. We prove that the associated codes achieve a recovery threshold similar to the ones for characteristic zero fields but now with a factor that is proportional to the genus of the underlying function field. In particular, we have that the recovery threshold of these codes is proportional to the classical complexity of matrix multiplication by a factor of at most the genus.

Virginia Tech (US)
National Science Foundation
Openalex Percentile: Top 16%
Advanced Data Storage Technologies
1.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.