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Poster
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Nikolas Ioannou · Celestine Mendler-Dünner · Thomas Parnell

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #39
in Algorithms -- Large Scale Learning »

In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability. We start by studying a state-of-the-art parallel implementation of SCD and identify scalability as well as system-level performance bottlenecks of the respective implementation. We then take a principled approach to develop a new SCD variant which is designed to avoid the identified system bottlenecks, such as limited scaling due to coherence traffic of model sharing across threads, and inefficient CPU cache accesses. Our proposed system-aware parallel coordinate descent algorithm (SySCD) scales to many cores and across numa nodes, and offers a consistent bottom line speedup in training time of up to x12 compared to an optimized asynchronous parallel SCD algorithm and up to x42, compared to state-of-the-art GLM solvers (scikit-learn, Vowpal Wabbit, and H2O) on a range of datasets and multi-core CPU architectures.

Keywords: Hardware and Systems · Applications · Stochastic Optimization · Algorithms -> Large Scale Learning; Optimization -> Convex Optimization; Optimization
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Author Information

Nikolas Ioannou (IBM Research)
Celestine Mendler-Dünner (UC Berkeley)
Thomas Parnell (IBM Research)

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