Quantum mechanics

Algorithm

Statistics

Erasure code

Block matrix

Theoretical computer science

Computation

Eigenvalues and eigenvectors

Parallel computing

Coding (social sciences)

Computer science

Decoding methods

Physics

Mathematics

Leverage (statistics)

Programming language

Machine learning

In distributed computing systems, it is well recognized that worker nodes that are slow (called stragglers) tend to dominate the overall job execution time. Coded computation utilizes concepts from erasure coding to mitigate the effect of stragglers by running “coded” copies of tasks comprising a job. Stragglers are typically treated as erasures in this process. While this is useful, there are issues with applying, e.g., MDS codes in a straightforward manner. Specifically, several applications such as matrix-vector products deal with sparse matrices. MDS codes typically require dense linear combinations of submatrices of the original matrix which destroy their inherent sparsity. This is problematic as it results in significantly higher processing times for computing the submatrix-vector products in coded computation. Furthermore, it also ignores partial computations at stragglers. In this work, we propose a fine-grained model that quantifies the level of non-trivial coding needed to obtain the benefits of coding in matrix-vector computation. Simultaneously, it allows us to leverage partial computations performed by the straggler nodes. For this model, we propose and evaluate several code designs and discuss their properties.

C<sup>3</sup>LES: Codes for Coded Computation that Leverage Stragglers

C3LES: Codes for Coded Computation that Leverage Stragglers

C³LES: Codes for Coded Computation that Leverage Stragglers