COMPaaS DLV: Composable Infrastructure for Deep Learning in an Academic Research Environment
Authors: Brown, M., Renambot, L., Long, L., Bargo, T., Johnson, A.
Publication: MERIT (Midscale Education and Research Infrastructure and Tools) Community Event Workshop, 27th IEEE International Conference on Network Protocols (ICNP 2019), Chicago, IL
In today’s Big Data era, data scientists require new computational instruments in order to quickly analyze large-scale datasets using complex codes and quicken the rate of scientific progress. While Federally-funded computer resources, from supercomputers to clouds, are beneficial, they are often limiting - particularly for deep learning and visualization - as they have few Graphics Processing Units (GPUs). GPUs are at the center of modern high-performance computing and artificial intelligence, efficiently performing mathematical operations that can be massively parallelized, speeding up codes used for deep learning, visualization and image processing, more so than general-purpose microprocessors, or Central Processing Units (CPUs). The University of Illinois at Chicago is acquiring a much-in-demand GPU-based instrument, COMPaaS DLV - COMposable Platform as a Service Instrument for Deep Learning & Visualization, based on composable infrastructure, an advanced architecture that disaggregates the underlying compute, storage, and network resources for scaling needs, but operates as a single cohesive infrastructure for management and workload purposes. We are experimenting with a small system and learning a great deal about composability, and we believe COMPaaS DLV users will benefit from the varied workflow that composable infrastructure allows.
Keywords: distributed systems, testbed implementation & deployment, composable infrastructure, deep learning, visualization
Date: October 7, 2019
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