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Memo regarding the Final Review of FY21 ASC L2 Milestone 7840: Neural Mini-Apps for Future Heterogeneous HPC Systems

Oldfield, Ron A.; Plimpton, Steven J.; Laros, James H.; Poliakoff, David Z.; Sornborger, Andrew S.

The final review for the FY21 Advanced Simulation and Computing (ASC) Computational Systems and Software Environments (CSSE) L2 Milestone #7840 was conducted on August 25th, 2021 at Sandia National Laboratories in Albuquerque, New Mexico. The review committee/panel unanimously agreed that the milestone has been successfully completed, exceeding expectations on several of the key deliverables.

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SPARC: Demonstrate burst-buffer-based checkpoint/restart on ATS-1

Oldfield, Ron A.; Ulmer, Craig D.; Widener, Patrick W.; Ward, Harry L.

Recent high-performance computing (HPC) platforms such as the Trinity Advanced Technology System (ATS-1) feature burst buffer resources that can have a dramatic impact on an application’s I/O performance. While these non-volatile memory (NVM) resources provide a new tier in the storage hierarchy, developers must find the right way to incorporate the technology into their applications in order to reap the benefits. Similar to other laboratories, Sandia is actively investigating ways in which these resources can be incorporated into our existing libraries and workflows without burdening our application developers with excessive, platform-specific details. This FY18Q1 milestone summaries our progress in adapting the Sandia Parallel Aerodynamics and Reentry Code (SPARC) in Sandia’s ATDM program to leverage Trinity’s burst buffers for checkpoint/restart operations. We investigated four different approaches with varying tradeoffs in this work: (1) simply updating job script to use stage-in/stage out burst buffer directives, (2) modifying SPARC to use LANL’s hierarchical I/O (HIO) library to store/retrieve checkpoints, (3) updating Sandia’s IOSS library to incorporate the burst buffer in all meshing I/O operations, and (4) modifying SPARC to use our Kelpie distributed memory library to store/retrieve checkpoints. Team members were successful in generating initial implementation for all four approaches, but were unable to obtain performance numbers in time for this report (reasons: initial problem sizes were not large enough to stress I/O, and SPARC refactor will require changes to our code). When we presented our work to the SPARC team, they expressed the most interest in the second and third approaches. The HIO work was favored because it is lightweight, unobtrusive, and should be portable to ATS-2. The IOSS work is seen as a long-term solution, and is favored because all I/O work (including checkpoints) can be deferred to a single library.

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Final Review of FY17 ASC CSSE L2 Milestone #6018 entitled "Analyzing Power Usage Characteristics of Workloads Running on Trinity"

Hoekstra, Robert J.; Hammond, Simon D.; Hemmert, Karl S.; Gentile, Ann C.; Oldfield, Ron A.; Lang, Mike L.; Martin, Steve M.

The presentation documented the technical approach of the team and summary of the results with sufficient detail to demonstrate both the value and the completion of the milestone. A separate SAND report was also generated with more detail to supplement the presentation.

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Delta: Data Reduction for Integrated Application Workflows

Lofstead, Gerald F.; Jean-Baptiste, Gregory J.; Oldfield, Ron A.

Integrated Application Workflows (IAWs) run multiple simulation workflow components con- currently on an HPC resource connecting these components using compute area resources and compensating for any performance or data processing rate mismatches. These IAWs require high frequency and high volume data transfers between compute nodes and staging area nodes during the lifetime of a large parallel computation. The available network band- width between the two areas may not be enough to efficiently support the data movement. As the processing power available to compute resources increases, the requirements for this data transfer will become more difficult to satisfy and perhaps will not be satisfiable at all since network capabilities are not expanding at a comparable rate. Furthermore, energy consumption in HPC environments is expected to grow by an order of magnitude as exas- cale systems become a reality. The energy cost of moving large amounts of data frequently will contribute to this issue. It is necessary to reduce the volume of data without reducing the quality of data when it is being processed and analyzed. Delta resolves the issue by addressing the lifetime data transfer operations. Delta removes subsequent identical copies of already transmitted data during transfers and restores those copies once the data has reached the destination. Delta is able to identify duplicated information and determine the most space efficient way to represent it. Initial tests show about 50% reduction in data movement while maintaining the same data quality and transmission frequency.

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Storage Systems and Input/Output to Support Extreme Scale Science

Ross, Robert B.; Ross, Robert B.; Grider, Gary G.; Grider, Gary G.; Felix, Evan F.; Felix, Evan F.; Gary, Mark G.; Gary, Mark G.; Klasky, Scott K.; Klasky, Scott K.; Oldfield, Ron A.; Oldfield, Ron A.; Shipman, Galen S.; Shipman, Galen S.; Wu, John W.; Wu, John W.

Storage systems are a foundational component of computational, experimental, and observational science today. The success of Department of Energy (DOE) activities in these areas is inextricably tied to the usability, performance, and reliability of storage and input/output (I/O) technologies.

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Formal metrics for large-scale parallel performance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Moreland, Kenneth D.; Oldfield, Ron A.

Performance measurement of parallel algorithms is well studied and well understood. However, a flaw in traditional performance metrics is that they rely on comparisons to serial performance with the same input. This comparison is convenient for theoretical complexity analysis but impossible to perform in large-scale empirical studies with data sizes far too large to run on a single serial computer. Consequently, scaling studies currently rely on ad hoc methods that, although effective, have no grounded mathematical models. In this position paper we advocate using a rate-based model that has a concrete meaning relative to speedup and efficiency and that can be used to unify strong and weak scaling studies.

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Results 1–25 of 95
Results 1–25 of 95