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Neural Networks and Graph Algorithms with Next-Generation Processors

by Kathleen E Hamilton, Catherine D Schuman, Steven R Young, Neena Imam, Travis S Humble
Publication Type
Conference Paper
Book Title
2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Publication Date
Page Numbers
1194 to 1203
Issue
0
Publisher Location
New Jersey, United States of America
Conference Name
GraML Workshop at IPDPS 2018
Conference Location
Vancouver, Canada
Conference Sponsor
IEEE
Conference Date
-

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.