Distributed Matrix Multiplication Using Speed Adaptive Coding
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While performing distributed computations in today's cloud-based platforms, execution speed variations among compute nodes can significantly reduce the performance and create bottlenecks like stragglers. Coded computation techniques leverage coding theory to inject computational redundancy and mitigate stragglers in distributed computations. In this paper, we propose a dynamic workload distribution strategy for coded computation called Slack Squeeze Coded Computation (S^2C^2). S^2C^2 squeezes the compute slack (i.e., overhead) that is built into the coded computing frameworks by efficiently assigning work for all fast and slow nodes according to their speeds and without needing to re-distribute data. We implement an LSTM-based speed prediction algorithm to predict speeds of compute nodes. We evaluate S^2C^2 on linear algebraic algorithms, gradient descent, graph ranking, and graph filtering algorithms. We demonstrate a 19 computation latency using S^2C^2 compared to job replication and coded computation. We further show how S^2C^2 can be applied beyond linear algebra.
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