Abstract
Memory technologies are under active development. Meanwhile, workloads on contemporary computing systems are increasing rapidly in size and diversity. Such dynamics in hardware and software further widen the gap between memory system design and performance evaluation. In this work, we propose a data-centric abstraction of high-performance computing applications for fast exploration of new memory technologies. We also provide a framework that uses a formal modeling language to describe the abstraction, automatically translates abstractions into memory traffic, and directly interfaces with cycle-accurate simulators. We evaluated the framework using 20 workloads and validated the memory traffic profile, the simulation results, and the relative memory changes of four memory technologies. Our results show that the data-centric abstraction can accurately capture application behavior adaptable to different input problems and can expedite system exploration.
Original language | English |
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Title of host publication | HPDC 2018 - Proceedings of the 2018 International Symposium on High-Performance Parallel and Distributed Computing |
Publisher | Association for Computing Machinery, Inc |
Pages | 180-191 |
Number of pages | 12 |
ISBN (Electronic) | 9781450357852 |
DOIs | |
State | Published - Jun 11 2018 |
Event | 27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018 - Tempe, United States Duration: Jun 11 2018 → Jun 15 2018 |
Publication series
Name | HPDC 2018 - Proceedings of the 2018 International Symposium on High-Performance Parallel and Distributed Computing |
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Conference
Conference | 27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018 |
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Country/Territory | United States |
City | Tempe |
Period | 06/11/18 → 06/15/18 |
Funding
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC05-00OR22725 This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education.
Keywords
- Application Abstraction
- Data-centric Modeling
- Memory Simulation