Academic Journal
Tools for Reduced Precision Computation: A Survey.
| Τίτλος: | Tools for Reduced Precision Computation: A Survey. |
|---|---|
| Συγγραφείς: | CHERUBIN, STEFANO, AGOSTA, GIOVANNI |
| Πηγή: | ACM Computing Surveys; Mar2021, Vol. 53 Issue 2, p1-35, 35p |
| Θεματικοί όροι: | High performance computing, Customization, Automation |
| Περίληψη: | The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are considered. However, standard compiler frameworks do not support automated precision customization, and manual tuning and code transformation is the approach usually adopted in most domains. In recent years, research have been studying ways to improve the automation of this process. This article surveys this body of work, identifying the critical steps of this process, the most advanced tools available, and the open challenges in this research area. We conclude that, while several mature tools exist, there is still a gap to close, especially for tools based on static analysis rather than profiling, as well as for integration within mainstream, industrystrength compiler frameworks. [ABSTRACT FROM AUTHOR] |
| Copyright of ACM Computing Surveys is the property of Association for Computing Machinery 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Tools for Reduced Precision Computation: A Survey. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22CHERUBIN%2C+STEFANO%22">CHERUBIN, STEFANO</searchLink><br /><searchLink fieldCode="AR" term="%22AGOSTA%2C+GIOVANNI%22">AGOSTA, GIOVANNI</searchLink> – Name: TitleSource Label: Source Group: Src Data: ACM Computing Surveys; Mar2021, Vol. 53 Issue 2, p1-35, 35p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22High+performance+computing%22">High performance computing</searchLink><br /><searchLink fieldCode="DE" term="%22Customization%22">Customization</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are considered. However, standard compiler frameworks do not support automated precision customization, and manual tuning and code transformation is the approach usually adopted in most domains. In recent years, research have been studying ways to improve the automation of this process. This article surveys this body of work, identifying the critical steps of this process, the most advanced tools available, and the open challenges in this research area. We conclude that, while several mature tools exist, there is still a gap to close, especially for tools based on static analysis rather than profiling, as well as for integration within mainstream, industrystrength compiler frameworks. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ACM Computing Surveys is the property of Association for Computing Machinery 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=160618631 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3381039 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 1 Subjects: – SubjectFull: High performance computing Type: general – SubjectFull: Customization Type: general – SubjectFull: Automation Type: general Titles: – TitleFull: Tools for Reduced Precision Computation: A Survey. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: CHERUBIN, STEFANO – PersonEntity: Name: NameFull: AGOSTA, GIOVANNI IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 03600300 Numbering: – Type: volume Value: 53 – Type: issue Value: 2 Titles: – TitleFull: ACM Computing Surveys Type: main |
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