  We perform the first study of the tradeoff space of access methods and
replication to support statistical analytics using first-order methods executed
in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical
analytics systems differ from conventional SQL-analytics in the amount and
types of memory incoherence they can tolerate. Our goal is to understand
tradeoffs in accessing the data in row- or column-order and at what granularity
one should share the model and data for a statistical task. We study this new
tradeoff space, and discover there are tradeoffs between hardware and
statistical efficiency. We argue that our tradeoff study may provide valuable
information for designers of analytics engines: for each system we consider,
our prototype engine can run at least one popular task at least 100x faster. We
conduct our study across five architectures using popular models including
SVMs, logistic regression, Gibbs sampling, and neural networks.
