Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers
Journal article, 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.

computational delay

Block-diagonal coding

distributed computing

maximum distance separable codes

straggling servers.

decoding delay

Luby Transform codes

machine learning algorithms


Albin Severinson

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Simula UiB

Alexandre Graell i Amat

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Eirik Rosnes

Simula UiB

IEEE Transactions on Communications

00906778 (ISSN)

Vol. 67 3 1739-1753

Rethinking Distributed Storage for Data Storage and Wireless Content Delivery

Swedish Research Council (VR), 2016-01-01 -- 2019-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Computer and Information Science


Communication Systems



More information

Latest update