SST-GPU: A Scalable SST GPU Component for Performance Modeling and Profiling

Technical Report · Wed Jan 13 04:00:00 EST 2021

DOI:https://doi.org/10.2172/1762830· OSTI ID:1762830

Hughes, Clayton ^[1]; Hammond, Simon David ^[1]; Hoekstra, Robert J. ^[1]; Zhang, Mengchi ^[2]; Liu, Yechen ^[2]; Rogers, Tim ^[2]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Purdue Univ., West Lafayette, IN (United States)

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of unprecedented amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1762830
• Report Number(s): SAND2021-0325; 693493
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING