Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers
Artikel i vetenskaplig tidskrift, 2019

We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set of vectors. The first scheme is based on partitioning the matrix into submatrices and applying maximum distance separable (MDS) codes to each submatrix. For this scheme, we prove that up to a given number of partitions the communication load and the computational delay (not including the encoding and decoding delay) are identical to those of the scheme recently proposed by Li et al., based on a single, long MDS code. However, due to the use of shorter MDS codes, our scheme yields a significantly lower overall computational delay when the delay incurred by encoding and decoding is also considered. We further propose a second coded scheme based on Luby Transform (LT) codes under inactivation decoding. Interestingly, LT codes may reduce the delay over the partitioned scheme at the expense of an increased communication load. We also consider distributed computing under a deadline and show numerically that the proposed schemes outperform other schemes in the literature, with the LT code-based scheme yielding the best performance.

Block-diagonal coding

maximum distance separable codes

distributed computing

straggling servers.

machine learning algorithms

computational delay

Luby Transform codes

decoding delay


Albin Severinson

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Simula UiB

Alexandre Graell i Amat

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Eirik Rosnes

Simula UiB

IEEE Transactions on Communications

00906778 (ISSN) 15580857 (eISSN)

Vol. 67 3 1739-1753 8502151

Distribuerad lagring för datalagring och trådlös leverans av data

Vetenskapsrådet (VR) (2016-04253), 2016-01-01 -- 2019-12-31.


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