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Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization

Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML). However, the scalability of these optimization methods is inhibited by the cost of communicating and synchronizing processors in a parallel setting. Iterative ML methods are particularly sensitive to communication cost since they often require communication every iteration. In this work, we extend well-known techniques from Communication-Avoiding Krylov subspace methods to first-order, block coordinate descent methods for Support Vector Machines and Proximal Least-Squares problems. Our Synchronization-Avoiding (SA) variants reduce the latency cost by a tunable factor of $s$ at the expense of a factor of $s$ increase in flops and bandwidth costs. We show that the SA-variants are numerically stable and can attain large speedups of up to $5.1\times$ on a Cray XC30 supercomputer.

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