Partitioning Strategies for Concurrent Programming
This work presents four partitioning strategies, or design patterns, useful for decomposing a serial application into multiple concurrently executing parts. These partitioning strategies augment the commonly used task and data parallel design patterns by recognizing that applications are spatiotem...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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2010
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| Online Access: | http://hdl.handle.net/1721.1/59845 https://orcid.org/0000-0001-8253-7714 https://orcid.org/0000-0002-7015-4262 |
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| author | Hoffmann, Henry Christian Agarwal, Anant Devadas, Srinivas |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hoffmann, Henry Christian Agarwal, Anant Devadas, Srinivas |
| author_sort | Hoffmann, Henry Christian |
| collection | MIT |
| description | This work presents four partitioning strategies, or design patterns, useful for decomposing a serial application
into multiple concurrently executing parts. These partitioning strategies augment the commonly used task and data
parallel design patterns by recognizing that applications are spatiotemporal in nature. Therefore, data and instruction
decomposition are further distinguished by whether the partitioning is done in the spatial or in temporal dimension.
Thus, this work describes four decomposition strategies: spatial data partitioning (SDP), temporal data partitioning
(TDP), spatial instruction partitioning (SIP), and temporal instruction partitioning (TIP), while cataloging the benefits
and drawbacks of each. These strategies can be combined to realize the benefits of multiple patterns in the same
program. The practical use of the partitioning strategies is demonstrated through a case study which implements
several different parallelizations of a multicore H.264 encoder for HD video. This case study illustrates the application of the patterns, their effects on the performance of the encoder, and the combination of multiple strategies in a single program. |
| first_indexed | 2024-09-23T10:48:21Z |
| format | Article |
| id | mit-1721.1/59845 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:48:21Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | mit-1721.1/598452022-09-27T15:06:36Z Partitioning Strategies for Concurrent Programming Hoffmann, Henry Christian Agarwal, Anant Devadas, Srinivas Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Devadas, Srinivas Hoffmann, Henry Christian Agarwal, Anant Devadas, Srinivas This work presents four partitioning strategies, or design patterns, useful for decomposing a serial application into multiple concurrently executing parts. These partitioning strategies augment the commonly used task and data parallel design patterns by recognizing that applications are spatiotemporal in nature. Therefore, data and instruction decomposition are further distinguished by whether the partitioning is done in the spatial or in temporal dimension. Thus, this work describes four decomposition strategies: spatial data partitioning (SDP), temporal data partitioning (TDP), spatial instruction partitioning (SIP), and temporal instruction partitioning (TIP), while cataloging the benefits and drawbacks of each. These strategies can be combined to realize the benefits of multiple patterns in the same program. The practical use of the partitioning strategies is demonstrated through a case study which implements several different parallelizations of a multicore H.264 encoder for HD video. This case study illustrates the application of the patterns, their effects on the performance of the encoder, and the combination of multiple strategies in a single program. 2010-11-05T19:58:08Z 2010-11-05T19:58:08Z 2009 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/59845 Devadas, Srinivas, Anant Agarwal, and Henry Hoffmann. "Partitioning Strategies for Concurrent Programming." https://orcid.org/0000-0001-8253-7714 https://orcid.org/0000-0002-7015-4262 en_US Attribution-Noncommercial-Share Alike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf MIT web domain |
| spellingShingle | Hoffmann, Henry Christian Agarwal, Anant Devadas, Srinivas Partitioning Strategies for Concurrent Programming |
| title | Partitioning Strategies for Concurrent Programming |
| title_full | Partitioning Strategies for Concurrent Programming |
| title_fullStr | Partitioning Strategies for Concurrent Programming |
| title_full_unstemmed | Partitioning Strategies for Concurrent Programming |
| title_short | Partitioning Strategies for Concurrent Programming |
| title_sort | partitioning strategies for concurrent programming |
| url | http://hdl.handle.net/1721.1/59845 https://orcid.org/0000-0001-8253-7714 https://orcid.org/0000-0002-7015-4262 |
| work_keys_str_mv | AT hoffmannhenrychristian partitioningstrategiesforconcurrentprogramming AT agarwalanant partitioningstrategiesforconcurrentprogramming AT devadassrinivas partitioningstrategiesforconcurrentprogramming |