  Data compression is a popular technique for improving the efficiency of data
processing workloads such as SQL queries and more recently, machine learning
(ML) with classical batch gradient methods. But the efficacy of such ideas for
mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm
of modern ML, is an open question. MGD's unique data access pattern renders
prior art, including those designed for batch gradient methods, less effective.
We fill this crucial research gap by proposing a new lossless compression
scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely
source, the string/text compression scheme Lempel-Ziv-Welch, but tailored to
MGD in a way that preserves tuple boundaries within mini-batches. We then
present a suite of novel compressed matrix operation execution techniques
tailored to the TOC compression scheme that operate directly over the
compressed data representation and avoid decompression overheads. An extensive
empirical evaluation with real-world datasets shows that TOC consistently
achieves substantial compression ratios by up to 51x and reduces runtimes for
MGD workloads by up to 10.2x in popular ML systems.
