Academic Journal
Locality-aware task scheduling for homogeneous parallel computing systems.
| Τίτλος: | Locality-aware task scheduling for homogeneous parallel computing systems. |
|---|---|
| Συγγραφείς: | Bhatti, Muhammad Khurram1 khurram.bhatti@itu.edu.pk, Oz, Isil2, Amin, Sarah1, Mushtaq, Maria1, Farooq, Umer3, Popov, Konstantin4, Brorsson, Mats5 |
| Πηγή: | Computing. Jun2018, Vol. 100 Issue 6, p557-595. 39p. |
| Θεματικοί όροι: | *Production scheduling, *Energy consumption, Parallel programs (Computer programs), Cache memory, Heuristic algorithms |
| Περίληψη: | In systems with complex many-core cache hierarchy, exploiting data locality can significantly reduce execution time and energy consumption of parallel applications. Locality can be exploited at various hardware and software layers. For instance, by implementing private and shared caches in a multi-level fashion, recent hardware designs are already optimised for locality. However, this would all be useless if the software scheduling does not cast the execution in a manner that promotes locality available in the programs themselves. Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures. This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS). The LeTS heuristic is a work-conserving algorithm that takes into account both locality and load balancing in order to reduce the execution time of target applications. The working principle of LeTS is based on two distinctive phases, namely; working task group formation phase (WTG-FP) and working task group ordering phase (WTG-OP). The WTG-FP forms groups of tasks in order to capture data reuse across tasks while the WTG-OP determines an optimal order of execution for task groups that minimizes the reuse distance of shared data between tasks. We have performed experiments using randomly generated task graphs by varying three major performance parameters, namely: (1) communication to computation ratio (CCR) between 0.1 and 1.0, (2) application size, i.e., task graphs comprising of 50-, 100-, and 300-tasks per graph, and (3) number of cores with 2-, 4-, 8-, and 16-cores execution scenarios. We have also performed experiments using selected real-world applications. The LeTS heuristic reduces overall execution time of applications by exploiting inter-task data locality. Results show that LeTS outperforms state-of-the-art algorithms in amortizing inter-task communication cost. [ABSTRACT FROM AUTHOR] |
| Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Locality-aware task scheduling for homogeneous parallel computing systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bhatti%2C+Muhammad+Khurram%22">Bhatti, Muhammad Khurram</searchLink><relatesTo>1</relatesTo><i> khurram.bhatti@itu.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Oz%2C+Isil%22">Oz, Isil</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Amin%2C+Sarah%22">Amin, Sarah</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mushtaq%2C+Maria%22">Mushtaq, Maria</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Farooq%2C+Umer%22">Farooq, Umer</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Popov%2C+Konstantin%22">Popov, Konstantin</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Brorsson%2C+Mats%22">Brorsson, Mats</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computing%22">Computing</searchLink>. Jun2018, Vol. 100 Issue 6, p557-595. 39p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programs+%28Computer+programs%29%22">Parallel programs (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Cache+memory%22">Cache memory</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In systems with complex many-core cache hierarchy, exploiting data locality can significantly reduce execution time and energy consumption of parallel applications. Locality can be exploited at various hardware and software layers. For instance, by implementing private and shared caches in a multi-level fashion, recent hardware designs are already optimised for locality. However, this would all be useless if the software scheduling does not cast the execution in a manner that promotes locality available in the programs themselves. Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures. This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS). The LeTS heuristic is a work-conserving algorithm that takes into account both locality and load balancing in order to reduce the execution time of target applications. The working principle of LeTS is based on two distinctive phases, namely; working task group formation phase (WTG-FP) and working task group ordering phase (WTG-OP). The WTG-FP forms groups of tasks in order to capture data reuse across tasks while the WTG-OP determines an optimal order of execution for task groups that minimizes the reuse distance of shared data between tasks. We have performed experiments using randomly generated task graphs by varying three major performance parameters, namely: (1) communication to computation ratio (CCR) between 0.1 and 1.0, (2) application size, i.e., task graphs comprising of 50-, 100-, and 300-tasks per graph, and (3) number of cores with 2-, 4-, 8-, and 16-cores execution scenarios. We have also performed experiments using selected real-world applications. The LeTS heuristic reduces overall execution time of applications by exploiting inter-task data locality. Results show that LeTS outperforms state-of-the-art algorithms in amortizing inter-task communication cost. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00607-017-0581-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 557 Subjects: – SubjectFull: Production scheduling Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Parallel programs (Computer programs) Type: general – SubjectFull: Cache memory Type: general – SubjectFull: Heuristic algorithms Type: general Titles: – TitleFull: Locality-aware task scheduling for homogeneous parallel computing systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bhatti, Muhammad Khurram – PersonEntity: Name: NameFull: Oz, Isil – PersonEntity: Name: NameFull: Amin, Sarah – PersonEntity: Name: NameFull: Mushtaq, Maria – PersonEntity: Name: NameFull: Farooq, Umer – PersonEntity: Name: NameFull: Popov, Konstantin – PersonEntity: Name: NameFull: Brorsson, Mats IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 0010485X Numbering: – Type: volume Value: 100 – Type: issue Value: 6 Titles: – TitleFull: Computing Type: main |
| ResultId | 1 |