Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication
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Large-scale machine learning and data mining applications require computer systems to perform massive computations that need to be parallelized across multiple nodes, for example, massive matrix-vector and matrix-matrix multiplication. The presence of straggling nodes -- computing nodes that unpredictably slowdown or fail -- is a major bottleneck in such distributed computations. We propose a rateless fountain coding strategy to alleviate the problem of stragglers in distributed matrix-vector multiplication. Our algorithm creates a stream of linear combinations of the m rows of the matrix, and assigns them to different worker nodes, which then perform row-vector products with the encoded rows. The original matrix-vector product can be decoded as soon as slightly more than m row-vector products are collectively finished by the nodes. This strategy enables fast nodes to steal work from slow nodes, without requiring the master to perform any dynamic load-balancing. Compared to recently proposed fixed-rate erasure coding strategies which ignore partial work done by straggling nodes, rateless coding achieves significantly lower overall delay, as well as small computational and decoding overhead.
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