HyMAC: A Hybrid Matrix Computation System

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Abstract

Distributed matrix computation is common in large-scale data processing and machine learning applications. Iterative-convergent algorithms involving matrix computation share a common property: parameters converge non-uniformly. This property can be exploited to avoid redundant computation via incremental evaluation. Unfortunately, existing systems that support distributed matrix computation, like SystemML, do not employ incremental evaluation. Moreover, incremental evaluation does not always outperform classical matrix computation, which we refer to as a full evaluation. To leverage the benefit of increments, we propose a new system called HyMAC, which performs hybrid plans to balance the trade-off between full and incremental evaluation at each iteration. In this demonstration, attendees will have an opportunity to experience the effect that full, incremental, and hybrid plans have on iterative algorithms.