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Title: Accelerating Scientific Computing in the Post-Moore’s Era

Abstract

Novel uses of graphical processing units for accelerated computation revolutionized the field of high-performance scientific computing by providing specialized workflows tailored to algorithmic requirements. As the era of Moore’s law draws to a close, many new non–von Neumann processors are emerging as potential computational accelerators, including those based on the principles of neuromorphic computing, tensor algebra, and quantum information. While development of these new processors is continuing to mature, the potential impact on accelerated computing is anticipated to be profound. We discuss how different processing models can advance computing in key scientific paradigms: machine learning and constraint satisfaction. Significantly, each of these new processor types utilizes a fundamentally different model of computation, and this raises questions about how to best use such processors 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. Furthermore, this also hints at the infrastructure needed to integrate next-generation processing units into future high-performance computing systems.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1649624
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
ACM Transactions on Parallel Computing
Additional Journal Information:
Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2329-4949
Publisher:
Association for Computing Machinery
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Graph algorithms; machine learning; constraint satisfaction problems; quantum computing; neuromorphic computing; optical Ising machines

Citation Formats

Hamilton, Kathleen E., Schuman, Catherine D., Young, Steven R., Bennink, Ryan S., Imam, Neena, and Humble, Travis S.. Accelerating Scientific Computing in the Post-Moore’s Era. United States: N. p., 2020. Web. https://doi.org/10.1145/3380940.
Hamilton, Kathleen E., Schuman, Catherine D., Young, Steven R., Bennink, Ryan S., Imam, Neena, & Humble, Travis S.. Accelerating Scientific Computing in the Post-Moore’s Era. United States. https://doi.org/10.1145/3380940
Hamilton, Kathleen E., Schuman, Catherine D., Young, Steven R., Bennink, Ryan S., Imam, Neena, and Humble, Travis S.. Sun . "Accelerating Scientific Computing in the Post-Moore’s Era". United States. https://doi.org/10.1145/3380940. https://www.osti.gov/servlets/purl/1649624.
@article{osti_1649624,
title = {Accelerating Scientific Computing in the Post-Moore’s Era},
author = {Hamilton, Kathleen E. and Schuman, Catherine D. and Young, Steven R. and Bennink, Ryan S. and Imam, Neena and Humble, Travis S.},
abstractNote = {Novel uses of graphical processing units for accelerated computation revolutionized the field of high-performance scientific computing by providing specialized workflows tailored to algorithmic requirements. As the era of Moore’s law draws to a close, many new non–von Neumann processors are emerging as potential computational accelerators, including those based on the principles of neuromorphic computing, tensor algebra, and quantum information. While development of these new processors is continuing to mature, the potential impact on accelerated computing is anticipated to be profound. We discuss how different processing models can advance computing in key scientific paradigms: machine learning and constraint satisfaction. Significantly, each of these new processor types utilizes a fundamentally different model of computation, and this raises questions about how to best use such processors 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. Furthermore, this also hints at the infrastructure needed to integrate next-generation processing units into future high-performance computing systems.},
doi = {10.1145/3380940},
journal = {ACM Transactions on Parallel Computing},
number = 1,
volume = 7,
place = {United States},
year = {2020},
month = {3}
}

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journal, November 2014

  • Cabessa, JÉRÉMie; Siegelmann, Hava T.
  • International Journal of Neural Systems, Vol. 24, Issue 08
  • DOI: 10.1142/S0129065714500294

Label propagation through neuronal synchrony
conference, July 2010

  • Quiles, Marcos G.; Zhao, Liang; Breve, Fabricio A.
  • The 2010 International Joint Conference on Neural Networks (IJCNN)
  • DOI: 10.1109/IJCNN.2010.5596809

A Fast Learning Algorithm for Deep Belief Nets
journal, July 2006


Demonstration of a small programmable quantum computer with atomic qubits
journal, August 2016


A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers
conference, November 2016

  • Potok, Thomas E.; Schuman, Catherine D.; Young, Steven R.
  • 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC)
  • DOI: 10.1109/MLHPC.2016.009

Deep learning in neural networks: An overview
journal, January 2015


Temperature Scaling Law for Quantum Annealing Optimizers
journal, September 2017


Identifying the minor set cover of dense connected bipartite graphs via random matching edge sets
journal, February 2017


Community detection in graphs
journal, February 2010


Local resolution-limit-free Potts model for community detection
journal, April 2010


Error mitigation extends the computational reach of a noisy quantum processor
journal, March 2019


Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
journal, June 2018

  • Perdomo-Ortiz, Alejandro; Benedetti, Marcello; Realpe-Gómez, John
  • Quantum Science and Technology, Vol. 3, Issue 3
  • DOI: 10.1088/2058-9565/aab859

Minor-embedding in adiabatic quantum computation: I. The parameter setting problem
journal, September 2008