A Comprehensive Study of In-Memory Computing on Large HPC Systems
| Autor: | Qing Liu, Dan Huang, Scott Klasky, Norbert Podhorszki, Zhenlu Qin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 020206 networking & telecommunications Usability 02 engineering and technology Supercomputer Data science Domain (software engineering) Dataspaces Software portability Workflow In-Memory Processing Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business |
| Zdroj: | ICDCS |
| Popis: | With the increasing fidelity and resolution enabled by high-performance computing systems, simulation-based scientific discovery is able to model and understand microscopic physical phenomena at a level that was not possible in the past. A grand challenge that the HPC community is faced with is how to handle the large amounts of analysis data generated from simulations. In-memory computing, among others, is recognized to be a viable path forward and has experienced tremendous success in the past decade. Nevertheless, there has been a lack of a complete study and understanding of in-memory computing as a whole on HPC systems. This paper presents a comprehensive study, which goes well beyond the typical performance metrics. In particular, we assess the in-memory computing with regard to its usability, portability, robustness and internal design trade-offs, which are the key factors that of interest to domain scientists. We use two realistic scientific workflows, LAMMPS and Laplace, to conduct comprehensive studies on state-of-the-art in-memory computing libraries, including DataSpaces, DIMES, Flexpath and Decaf. We conduct cross-platform experiments at scale on two leading supercomputers, Titan at ORNL and Cori at NERSC, and summarize our key findings in this critical area. |
| Databáze: | OpenAIRE |
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