Abstract
Successfully exploiting distributed collections of heterogeneous many-cores architectures with complex memory hierarchy through a portable programming model is a challenge for application developers. The literature is not short of proposals addressing this problem, including many evolutionary solutions that seek to extend the capabilities of current message passing paradigms with intranode features (MPI+X). A different, more revolutionary, solution explores data-flow task-based runtime systems as a substitute to both local and distributed data dependencies management. The solution explored in this paper, PaRSEC, is based on such a programming paradigm, supported by a highly efficient task-based runtime. This paper compares two programming paradigms present in PaRSEC, Parameterized Task Graph (PTG) and Dynamic Task Discovery (DTD) in terms of capabilities, overhead and potential benefits.
Original language | English |
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Title of host publication | Proceedings of ScalA 2017 |
Subtitle of host publication | 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis |
Publisher | Association for Computing Machinery, Inc |
ISBN (Print) | 9781450351256 |
DOIs | |
State | Published - Nov 12 2017 |
Event | 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA 2017 - Held in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 - Denver, United States Duration: Nov 12 2017 → Nov 17 2017 |
Publication series
Name | Proceedings of ScalA 2017: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
Conference | 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA 2017 - Held in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 |
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Country/Territory | United States |
City | Denver |
Period | 11/12/17 → 11/17/17 |
Funding
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.
Keywords
- Data-flow
- Dynamic task-graph
- PaRSEC
- Task-based runtime