Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication

Abstract

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of stragglers -- nodes that unpredictably slowdown or fail -- is a major bottleneck in such distributed computations. We propose a rateless fountain coding strategy to address this issue. Our idea is to create linear combinations of the m rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than m row-vector products are collectively finished by the nodes. We show that our approach achieves optimal latency and performs zero redundant computations asymptotically. Experiments on Amazon EC2 show that rateless coding gives as much as 3x speed-up over uncoded schemes.