Design of a Multithreaded Barnes-Hut Algorithm for Multicore Clusters
We describe in this paper an implementation of the Barnes-Hut algorithm on multicore clusters. Based on a partitioned global address space (PGAS) library, the design integrates intranode multithreading and internode one-sided communication, exemplifying a PGAS + X programming style. Within a node, t...
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| Veröffentlicht in: | IEEE transactions on parallel and distributed systems Jg. 26; H. 7; S. 1861 - 1873 |
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| Sprache: | Englisch |
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IEEE
01.07.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | We describe in this paper an implementation of the Barnes-Hut algorithm on multicore clusters. Based on a partitioned global address space (PGAS) library, the design integrates intranode multithreading and internode one-sided communication, exemplifying a PGAS + X programming style. Within a node, the computation is decomposed into tasks (subtasks) and multitasking is used to hide network latency. We study the tradeoffs between locality in private caches and locality in shared caches and bring the insights into the design. As a result, our implementation consumes less memory per core, invokes less internode communication, and enjoys better load-balancing strategies. The final code achieves up to 41 percent performance improvement over a non-multithreaded counterpart. Through detailed comparison, we also show its advantages over other well-known Barnes-Hut implementations, both in programming complexity and in performance. |
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| AbstractList | We describe in this paper an implementation of the Barnes-Hut algorithm on multicore clusters. Based on a partitioned global address space (PGAS) library, the design integrates intranode multithreading and internode one-sided communication, exemplifying a PGAS [Formula Omitted] X programming style. Within a node, the computation is decomposed into tasks (subtasks) and multitasking is used to hide network latency. We study the tradeoffs between locality in private caches and locality in shared caches and bring the insights into the design. As a result, our implementation consumes less memory per core, invokes less internode communication, and enjoys better load-balancing strategies. The final code achieves up to 41 percent performance improvement over a non-multithreaded counterpart. Through detailed comparison, we also show its advantages over other well-known Barnes-Hut implementations, both in programming complexity and in performance. We describe in this paper an implementation of the Barnes-Hut algorithm on multicore clusters. Based on a partitioned global address space (PGAS) library, the design integrates intranode multithreading and internode one-sided communication, exemplifying a PGAS + X programming style. Within a node, the computation is decomposed into tasks (subtasks) and multitasking is used to hide network latency. We study the tradeoffs between locality in private caches and locality in shared caches and bring the insights into the design. As a result, our implementation consumes less memory per core, invokes less internode communication, and enjoys better load-balancing strategies. The final code achieves up to 41 percent performance improvement over a non-multithreaded counterpart. Through detailed comparison, we also show its advantages over other well-known Barnes-Hut implementations, both in programming complexity and in performance. |
| Author | Snir, Marc Junchao Zhang Behzad, Babak |
| Author_xml | – sequence: 1 surname: Junchao Zhang fullname: Junchao Zhang email: jczhang@anl.gov organization: Math. & Comput. Sci. (MCS) Div., Argonne Nat. Lab., Argonne, IL, USA – sequence: 2 givenname: Babak surname: Behzad fullname: Behzad, Babak email: bbehza2@illinois.edu organization: Comput. Sci. Dept., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA – sequence: 3 givenname: Marc surname: Snir fullname: Snir, Marc email: snir@anl.gov organization: Math. & Comput. Sci. (MCS) Div., Argonne Nat. Lab., Argonne, IL, USA |
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| Title | Design of a Multithreaded Barnes-Hut Algorithm for Multicore Clusters |
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