Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Journal Article · · International Journal of High Performance Computing Applications
 [1];  [2];  [2];  [3];  [4];  [5];  [6];  [2];  [4];  [1];  [7];  [4];  [5];  [6];  [2];  [3];  [2];  [1];  [2];  [1] more »;  [2];  [2];  [5];  [8];  [9];  [10];  [11];  [3];  [12];  [6];  [6];  [4];  [11];  [2];  [2];  [5];  [11];  [1];  [5];  [8];  [5];  [11];  [4];  [2];  [1];  [1];  [1] « less
  1. Brookhaven National Laboratory (BNL), Upton, NY (United States)
  2. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  4. Sandia National Laboratory (SNL-NM), Albuquerque, NM (United States)
  5. Argonne National Laboratory (ANL), Lemont, IL (United States)
  6. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  7. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland)
  8. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  9. Institute of Physical and Chemical Research (RIKEN), Tokyo (Japan); Tokyo Institute of Technology (Japan)
  10. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); University of Oregon, Eugene, OR (United States)
  11. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  12. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Tokyo Institute of Technology (Japan)
+ Show Author Affiliations

We report rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); Japan Society for the Promotion of Science (JSPS)
Grant/Contract Number:
AC05-00OR22725; AC02-06CH11357; AC52-07NA27344; AC02-05CH11231; 89233218CNA000001
OSTI ID:
1890353
Alternate ID(s):
OSTI ID: 2311421
OSTI ID: 1894833
OSTI ID: 1915263
Report Number(s):
LA-UR--20-26880; PNNL-SA--156070
Journal Information:
International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Journal Issue: 6 Vol. 35; ISSN 1094-3420
Publisher:
SAGECopyright Statement
Country of Publication:
United States
Language:
English

References (33)

Photothermally Directed Assembly of Block Copolymers journal December 2019
The MolSSI QCA rchive project: An open‐source platform to compute, organize, and share quantum chemistry data journal July 2020
Materials Discovery and Design book January 2018
Layered convolutional dictionary learning for sparse coding itemsets journal May 2018
Optimal experimental design and some related control problems journal February 2008
Principal component transport in turbulent combustion: A posteriori analysis journal May 2015
Optimal experimental design for materials discovery journal March 2017
Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms journal June 2020
Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening journal January 2015
Optimization of Molecules via Deep Reinforcement Learning journal July 2019
A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering journal August 2019
Advances in Kriging-Based Autonomous X-Ray Scattering Experiments journal January 2020
A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters journal July 2020
Rapid ordering of block copolymer thin films journal August 2016
Solving large scale structure in ten easy steps with COLA journal June 2013
ExaWind: A multifidelity modeling and simulation environment for wind energy journal January 2020
Rocketsled: a software library for optimizing high-throughput computational searches journal April 2019
Exascale applications: skin in the game
  • Alexander, Francis; Almgren, Ann; Bell, John
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 378, Issue 2166 https://doi.org/10.1098/rsta.2019.0056
journal January 2020
GSAS-II : the genesis of a modern open-source all purpose crystallography software package journal March 2013
DLHub: Model and Data Serving for Science conference May 2019
Generative Adversarial Networks: An Overview journal January 2018
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
  • Mathuriya, Amrita; Bard, Deborah; Mendygral, Peter
  • SC18: International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1109/SC.2018.00068
conference November 2018
Quantifying the Objective Cost of Uncertainty in Complex Dynamical Systems journal May 2013
Multi-scale initial conditions for cosmological simulations: Multi-scale initial conditions journal July 2011
A Knowledge-Gradient Policy for Sequential Information Collection journal January 2008
LBANN: livermore big artificial neural network HPC toolkit conference January 2015
Parsl: Pervasive Parallel Programming in Python
  • Babuji, Yadu; Foster, Ian; Wilde, Michael
  • Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing - HPDC '19 https://doi.org/10.1145/3307681.3325400
conference January 2019
Online data analysis and reduction: An important Co-design motif for extreme-scale computers journal June 2021
CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research journal December 2018
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks journal May 2019
Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations journal August 2019
A data ecosystem to support machine learning in materials science journal October 2019
New Constraints on IGM Thermal Evolution from the Ly α Forest Power Spectrum journal February 2019

Similar Records

Related Subjects

97 MATHEMATICS AND COMPUTING
HPC for ML
ML for HPC
active learning
exascale computing
machine learning
reinforcement learning