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Title: Efficient Data Movement for Machine Learning


Abstract: Over the past decade, advances in machine learning
algorithms and models have enabled some remarkable applications.
However, these applications place considerable demands on our
computing infrastructure, incurring significant equipment costs and
processing delays. ML These developments have renewed the focus on
optimizing the underlying systems that dictate how efficiently models
can be trained and deployed. A vital aspect of system efficiency is
the efficient movement of data. ML workloads are intensely
data-driven, requiring vast amounts of data to be fed to accelerators
(GPUs, TPUs, etc.) for processing. Bottlenecks in data transfer,
whether between multiple accelerators or between memory and the
accelerator severely limit performance. Minimizing latency and
maximizing bandwidth through optimized interconnects and efficient
scheduling of compute and communications are crucial for improving ML
efficiency. In this talk, I will describe our recent work on efficient
orchestration of data movement in training and inference settings and
demonstrate how new ways of utilizing the hardware infrastructure can
yield significant performance gains.


Speaker bio: Arvind Krishnamurthy is the Short-Dooley Professor in the Paul G.
Allen School of Computer Science & Engineering. His research interests
are in building effective and robust computer systems in the context
of both data centers and Internet-scale systems. More recently, his
research has focussed on programmable networks and systems for machine
learning. He is an ACM fellow, a past program chair of ACM SIGCOMM and
Usenix NSDI, is a former Vice President of Usenix, and has served on
the CRA board.

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