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Scalable Task Parallel Programming in the Partitioned Global Address Space
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dinan, James |
| Copyright Year | 2010 |
| Abstract | Applications that exhibit irregular, dynamic, and unbalanced parallelism are growing in number and importance in the computational science and engineering communities. These applications span many domains including computational chemistry, physics, biology, and data mining. In such applications, the units of computation are often irregular in size and the availability of work may be depend on the dynamic, often recursive, behavior of the program. Because of these properties, it is challenging for these programs to achieve high levels of performance and scalability on modern high performance clusters. A new family of programming models, called the Partitioned Global Address Space (PGAS) family, provides the programmer with a global view of shared data and allows for asynchronous, one-sided access to data regardless of where it is physically stored. In this model, the global address space is distributed across the memories of multiple nodes and, for any given node, is partitioned into local patches that have high affinity and low access cost and remote patches that have a high access cost due to communication. The PGAS data model relaxes conventional two-sided communication semantics and allows the programmer to access remote data without the cooperation of the remote processor. Thus, this model is attractive for supporting irregular and dynamic applications on distributed memory clusters. However, in |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://etd.ohiolink.edu/!etd.send_file?accession=osu1275418061&disposition=inline |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
