Performance Analysis of Scientific Applications on an NVIDIA Grace System

The NSF funded Frontera system has supported many important scientific applications over the last five years, not only in HPC but also in the area of Big Data and Machine Learning. Applications leveraged the petascale computational capabilities of the system to achieve new breakthroughs in science a...

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Vydáno v:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis s. 558 - 566
Hlavní autoři: Ruhela, Amit, Cazes, John, McCalpin, John, Del-Castillo-Negrete, Carlos, Li, Junjie, Liu, Hang, Chen, Hanning, Lu, Chun-Yaung, Milfeld, Kent, Zhang, Wenyang, Wang, Ian, Koesterke, Lars, DeSantis, John, Lewis, Nic, Hempel, Sean, Stanzione, Dan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 17.11.2024
Témata:
Computer architecture
Grace-Hopper
Graphics processing units
Hardware
High performance computing
HPC
Lakes
Machine learning
NVIDIA
Performance analysis
Petascale
Production
Software
Supercomputers
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Abstract The NSF funded Frontera system has supported many important scientific applications over the last five years, not only in HPC but also in the area of Big Data and Machine Learning. Applications leveraged the petascale computational capabilities of the system to achieve new breakthroughs in science and engineering. Frontera is currently the fastest academic supercomputer in the US and is used by hundreds of scientists and engineers daily, enabling researchers to tackle more significant and complex challenges than ever before. As Frontera is nearing the end of its production life, it will be replaced by a new system, "Horizon". An intermediate system, "Vista", will bridge the gap by enabling researchers to access updated software and hardware technologies before Horizon is available. This paper summarizes early experiences on Vista by reporting on the performance of key applications and presenting its design and architecture. Early results are presented from the CPU architecture of Vista, "Grace-Grace", and compared with other processor technologies at TACC from Intel and AMD. A future paper will discuss the performance of the "Grace-Hopper" component of Vista.
AbstractList The NSF funded Frontera system has supported many important scientific applications over the last five years, not only in HPC but also in the area of Big Data and Machine Learning. Applications leveraged the petascale computational capabilities of the system to achieve new breakthroughs in science and engineering. Frontera is currently the fastest academic supercomputer in the US and is used by hundreds of scientists and engineers daily, enabling researchers to tackle more significant and complex challenges than ever before. As Frontera is nearing the end of its production life, it will be replaced by a new system, "Horizon". An intermediate system, "Vista", will bridge the gap by enabling researchers to access updated software and hardware technologies before Horizon is available. This paper summarizes early experiences on Vista by reporting on the performance of key applications and presenting its design and architecture. Early results are presented from the CPU architecture of Vista, "Grace-Grace", and compared with other processor technologies at TACC from Intel and AMD. A future paper will discuss the performance of the "Grace-Hopper" component of Vista.
Author Liu, Hang
Cazes, John
Stanzione, Dan
Lu, Chun-Yaung
Koesterke, Lars
Hempel, Sean
McCalpin, John
Lewis, Nic
Li, Junjie
DeSantis, John
Ruhela, Amit
Del-Castillo-Negrete, Carlos
Milfeld, Kent
Chen, Hanning
Zhang, Wenyang
Wang, Ian
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  givenname: Dan
  surname: Stanzione
  fullname: Stanzione, Dan
  email: dan@tacc.utexas.edu
  organization: Texas Advanced Computing Center,Austin,Texas
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Snippet The NSF funded Frontera system has supported many important scientific applications over the last five years, not only in HPC but also in the area of Big Data...
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SubjectTerms Computer architecture
Grace-Hopper
Graphics processing units
Hardware
High performance computing
HPC
Lakes
Machine learning
NVIDIA
Performance analysis
Petascale
Production
Software
Supercomputers
Title Performance Analysis of Scientific Applications on an NVIDIA Grace System
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