Thread- and Process-based Implementations of the pSystem Parallel Programming Environment
Run‐time work distribution in parallel programming systems is usually accomplished through the use of dynamic scheduling heuristics. Their sensitivity to run‐time information such as global work‐load, task granularity, data dependencies, locality of information, among others, is essential when tryin...
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| Published in: | Software, practice & experience Vol. 27; no. 3; pp. 329 - 351 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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New York
John Wiley & Sons, Ltd
01.03.1997
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| ISSN: | 0038-0644, 1097-024X |
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| Abstract | Run‐time work distribution in parallel programming systems is usually accomplished through the use of dynamic scheduling heuristics. Their sensitivity to run‐time information such as global work‐load, task granularity, data dependencies, locality of information, among others, is essential when trying to optimize performance. Adaptive schedulers that base their decisions on feed‐back from the system are therefore of special importance. We have developed and used a general purpose parallel programming system, the pSystem, that also served as a test‐bed environment on which we have experimented and studied the performance of distinct scheduling heuristics. Currently, we have two versions of the system: one based on Unix processes; and the other on Solaris threads. Threads (particularly user‐level threads) are usually associated with low execution overheads, since they require minimal interaction with the operating system kernel. This suggests that lower grain parallelism may be more effectively exploited with a thread‐based parallel programming system. Performance analysis of both implementations over a set of well known benchmarks, with various schedulers, shows that threads scale better under higher system loads and/or when the granularity of the tasks being executed is below a given threshold value. This paper starts with a description of the design and implementation of the pSystem computational model, followed by a detailed description of several experiments and the analysis of their results. © 1997 John Wiley & Sons, Ltd. |
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| AbstractList | Run-time work distribution in parallel programming systems is usually accomplished through the use of dynamic scheduling heuristics. Their sensitivity to run-time information such as global work-load, task granularity, data dependencies, locality of information, among others, is essential when trying to optimize performance. Adaptive schedulers that base their decisions on feed-back from the system are therefore of special importance. We have developed and used a general purpose parallel programming system, the pSystem, that also served as a test-bed environment on which we have experimented and studied the performance of distinct scheduling heuristics. Currently, we have two versions of the system: one based on Unix processes; and the other on Solaris threads. Threads (particularly user-level threads) are usually associated with low execution overheads, since they require minimal interaction with the operating system kernel. This suggests that lower grain parallelism may be more effectively exploited with a thread-based parallel programming system. Performance analysis of both implementations over a set of well known benchmarks, with various schedulers, shows that threads scale better under higher system loads and/or when the granularity of the tasks being executed is below a given threshold value. This paper starts with a description of the design and implementation of the pSystem computational model, followed by a detailed description of several experiments and the analysis of their results. Run‐time work distribution in parallel programming systems is usually accomplished through the use of dynamic scheduling heuristics. Their sensitivity to run‐time information such as global work‐load, task granularity, data dependencies, locality of information, among others, is essential when trying to optimize performance. Adaptive schedulers that base their decisions on feed‐back from the system are therefore of special importance. We have developed and used a general purpose parallel programming system, the pSystem, that also served as a test‐bed environment on which we have experimented and studied the performance of distinct scheduling heuristics. Currently, we have two versions of the system: one based on Unix processes; and the other on Solaris threads. Threads (particularly user‐level threads) are usually associated with low execution overheads, since they require minimal interaction with the operating system kernel. This suggests that lower grain parallelism may be more effectively exploited with a thread‐based parallel programming system. Performance analysis of both implementations over a set of well known benchmarks, with various schedulers, shows that threads scale better under higher system loads and/or when the granularity of the tasks being executed is below a given threshold value. This paper starts with a description of the design and implementation of the pSystem computational model, followed by a detailed description of several experiments and the analysis of their results. © 1997 John Wiley & Sons, Ltd. Run-time work distribution in parallel programming systems is usually accomplished through the use of dynamic scheduling heuristics. Their sensitivity to run-time information such as global work-load, task granularity, data dependencies, locality of information, among others, is essential when trying to optimize performance. Adaptive schedulers that base their decisions on feed-back from the system are therefore of special importance. The authors have developed a general purpose parallel programming system, the pSystem, which has served as a test-bed to study the performance of distinct scheduling heuristics. Currently, there are 2 versions of the system: one based on Unix processes; and the other on Solaris threads. Threads (particularly user-level threads) are usually associated with low-level execution overheads, since they require minimal interaction with the operating system kernel. This suggests that lower grain parallelism may be more effectively exploited with a thread-based parallel programming system. Performance analyses of both implementations over a set of well known benchmarks, with various schedulers, shows that threads scale better under higher system loads and/or when the granularity of the tasks being executed is below a given threshold value. (Abstract quotes from original text) |
| Author | LOPES, LUÍS M. B. SILVA, FERNANDO M. A. |
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| References | R. Olson, I. Foster and S. Tuecke, 'Productive parallel programming: the PCN approach', Scientific Programming, 1; 51-66, (1992). T. Casavant and J. Kuhl, 'A taxonomy of scheduling in general-purpose distributed computing systems', IEEE Transactions on Software Engineering, 14 (2), 141-154, (February 1988). M. Lam and M. Rinard, 'Coarse-grain parallel programming in Jade', ACM SIGPLAN Notices, 26 (7), 94-105, (July 1991). A. Grimshaw, 'Easy-to-use object-oriented parallel processing with Mentat', IEEE Computer, 26 (5), 39-51, (May 1993). E. Lazowska, D. Eager and J. Zahorjan, 'Adaptative load sharing in homogeneous distributed systems', IEEE Transactions on Software Engineering, 12 (5), 662-675, (May 1986). N. Carriero and D. Gelernter, How to Write Parallel Programs: A First Course, MIT Press, Cambridge, MA, 1990. V. Sunderam, 'PVM: A framework for parallel distributed computing', Journal of Concurrency: Practice and Experience, 2 (4), 315-339 (1990). 1993; 26 1990; 2 1991; 26 1990 1986; 12 1988; 14 1995 1994 1993 1992 1991 1992; 1 1988 |
| References_xml | – reference: T. Casavant and J. Kuhl, 'A taxonomy of scheduling in general-purpose distributed computing systems', IEEE Transactions on Software Engineering, 14 (2), 141-154, (February 1988). – reference: A. Grimshaw, 'Easy-to-use object-oriented parallel processing with Mentat', IEEE Computer, 26 (5), 39-51, (May 1993). – reference: R. Olson, I. Foster and S. Tuecke, 'Productive parallel programming: the PCN approach', Scientific Programming, 1; 51-66, (1992). – reference: E. Lazowska, D. Eager and J. Zahorjan, 'Adaptative load sharing in homogeneous distributed systems', IEEE Transactions on Software Engineering, 12 (5), 662-675, (May 1986). – reference: M. Lam and M. Rinard, 'Coarse-grain parallel programming in Jade', ACM SIGPLAN Notices, 26 (7), 94-105, (July 1991). – reference: V. Sunderam, 'PVM: A framework for parallel distributed computing', Journal of Concurrency: Practice and Experience, 2 (4), 315-339 (1990). – reference: N. Carriero and D. Gelernter, How to Write Parallel Programs: A First Course, MIT Press, Cambridge, MA, 1990. – start-page: 827 year: 1994 end-page: 830 – volume: 26 start-page: 39 issue: 5 year: 1993 end-page: 51 article-title: Easy‐to‐use object‐oriented parallel processing with Mentat publication-title: IEEE Computer – year: 1988 – year: 1995 – volume: 26 start-page: 94 issue: 7 year: 1991 end-page: 105 article-title: Coarse‐grain parallel programming in Jade publication-title: ACM SIGPLAN Notices – start-page: 502 year: 1990 end-page: 509 – volume: 2 start-page: 315 issue: 4 year: 1990 end-page: 339 article-title: PVM: A framework for parallel distributed computing publication-title: Journal of Concurrency: Practice and Experience – volume: 1 start-page: 51 year: 1992 end-page: 66 article-title: Productive parallel programming: the PCN approach publication-title: Scientific Programming – year: 1991 – start-page: 19 year: 1994 end-page: 32 – year: 1990 – year: 1993 – year: 1992 – volume: 12 start-page: 662 issue: 5 year: 1986 end-page: 675 article-title: Adaptative load sharing in homogeneous distributed systems publication-title: IEEE Transactions on Software Engineering – year: 1994 – volume: 14 start-page: 141 issue: 2 year: 1988 end-page: 154 article-title: A taxonomy of scheduling in general‐purpose distributed computing systems publication-title: IEEE Transactions on Software Engineering |
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| SubjectTerms | parallel programming environments parallelism performance analysis scheduling |
| Title | Thread- and Process-based Implementations of the pSystem Parallel Programming Environment |
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