  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.
