Award Abstract # 1927880
Category II : Ookami: A high-productivity path to frontiers of scientific discovery enabled by exascale system technologies

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Awardee: RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE
Initial Amendment Date: July 11, 2019
Latest Amendment Date: May 2, 2022
Award Number: 1927880
Award Instrument: Cooperative Agreement
Program Manager: Robert Chadduck
rchadduc@nsf.gov
 (703)292-2247
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Direct For Computer & Info Scie & Enginr
Start Date: October 1, 2019
End Date: September 30, 2024 (Estimated)
Total Intended Award Amount: $2,780,373.00
Total Awarded Amount to Date: $6,099,835.00
Funds Obligated to Date: FY 2019 = $3,336,446.00
FY 2020 = $1,668,226.00

FY 2021 = $556,073.00

FY 2022 = $539,090.00
History of Investigator:
  • Robert Harrison (Principal Investigator)
    robert.harrison@stonybrook.edu
  • Barbara Chapman (Co-Principal Investigator)
  • Matthew Jones (Co-Principal Investigator)
  • Alan Calder (Co-Principal Investigator)
Awardee Sponsored Research Office: SUNY at Stony Brook
WEST 5510 FRANKS MELVILLE MEMORI
STONY BROOK
NY  US  11794-0001
(631)632-9949
Sponsor Congressional District: 01
Primary Place of Performance: SUNY at Stony Brook
NY  US  11794-0001
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): M746VC6XMNH9
Parent UEI: GMZUKXFDJMA9
NSF Program(s): Innovative HPC
Primary Program Source:  
040100 NSF RESEARCH & RELATED ACTIVIT

040100 NSF RESEARCH & RELATED ACTIVIT

040100 NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 7619
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The State University of New York proposes to procure and operate for at least four years the first computer outside of Japan with the A64fx processor developed by Fujitsu for the Japanese path to exascale computing (i.e., computers capable of 10^18 operations per second). The ARM-based, multi-core, 512-bit SIMD-vector processor with ultrahigh-bandwidth memory promises to retain familiar and successful programming models while achieving very high performance for a wide range of applications including simulation and big data. The testbed significantly extends current NSF-sponsored HPC technologies and will enable the community to evaluate and demonstrate the potential of this technology for deployment in multiple settings. Through integration with NSF's Extreme Science and Engineering Discovery Environment (XSEDE), the system will be widely accessible and fully leverages existing cyber infrastructure including the XDMoD monitoring system.

What does this mean for science? Compared with the best CPUs anticipated during the deployment period, A64fx offers 2-4x better performance on memory-intensive applications such as sparse-matrix solvers found in many engineering and physics codes. For nearly all other applications, performance is also better or competitive. This transformational performance should be available nearly out of the box, with additional performance possible from tuning. To the scientist or engineer this means faster time to solution with significantly less programmer effort. The target class of applications to be enabled are memory-bandwidth intensive with 32GB/node memory, with significant gains anticipated for many other applications. Analysis of XSEDE workload shows 86% of all jobs (85% cycles) will fit within the available memory per node and that the majority of jobs are memory-bandwidth intensive. Finally, we have concrete plans to substantially broaden participation in science and engineering research by partnering with external organizations at the institutional, regional, and national levels.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Michalowicz, Benjamin and Raut, Eric and Kang, Yan and Curtis, Tony and Chapman, Barbara and Oryspayev, Dossay "Comparing the behavior of OpenMP Implementations with various Applications on two different Fujitsu A64FX platforms" PEARC '21: Practice and Experience in Advanced Research Computing , 2021 https://doi.org/10.1145/3437359.3465592 Citation Details
Burford, Andrew and Calder, Alan and Carlson, David and Chapman, Barbara and Coskun, Firat and Curtis, Tony and Feldman, Catherine and Harrison, Robert and Kang, Yan and Michalowicz, Benjamin and Raut, Eric and Siegmann, Eva and Wood, Daniel and DeLeon, R "Ookami: Deployment and Initial Experiences" PEARC '21: Practice and Experience in Advanced Research Computing , 2021 https://doi.org/10.1145/3437359.3465578 Citation Details
Feldman, Catherine and Michalowicz, Benjamin and Siegmann, Eva and Curtis, Tony and Calder, Alan and Harrison, Robert "Experiences with Porting the FLASH Code to Ookami, an HPE Apollo 80 A64FX Platform" HPCAsia 2022 Workshop: International Conference on High Performance Computing in Asia-Pacific Region Workshops , 2022 https://doi.org/10.1145/3503470.3503478 Citation Details
Michalowicz, Raut "Comparing OpenMP Implementations with Applications Across A64FX Platforms" IWOMP 2021: OpenMP: Enabling Massive Node-Level Parallelism , 2021 https://doi.org/10.1007/978-3-030-85262-7_9 Citation Details
Simakov, Nikolay A. and Deleon, Robert L. and Lin, Yuqing and Hoffmann, Phillip S. and Mathias, William R. "Developing Accurate Slurm Simulator" PEARC '22: Practice and Experience in Advanced Research Computing , 2022 https://doi.org/10.1145/3491418.3535178 Citation Details
Siegmann, Eva and Calder, Alan and Feldman, Catherine and Harrison, Robert J. "Educating HPC Users in the use of advanced computing technology" 2021 IEEE/ACM Ninth Workshop on Education for High Performance Computing (EduHPC) , 2021 https://doi.org/10.1109/EduHPC54835.2021.00008 Citation Details
Lu, Curtis "OpenSHMEM Active Message Extension for Task-Based Programming" OpenSHMEM 2021: OpenSHMEM and Related Technologies. OpenSHMEM in the Era of Exascale and Smart Networks , 2022 https://doi.org/10.1007/978-3-031-04888-3_8 Citation Details
Bari, Md Abdullah and Chapman, Barbara and Curtis, Anthony and Harrison, Robert J. and Siegmann, Eva and Simakov, Nikolay A. and Jones, Matthew D. "A64FX performance: experience on Ookami" 2021 IEEE International Conference on Cluster Computing (CLUSTER) , 2021 https://doi.org/10.1109/Cluster48925.2021.00106 Citation Details

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