Please use this identifier to cite or link to this item:
https://doi.org/10.3390/jlpea10040031
DC Field | Value | |
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dc.title | Pkmin: Peak power minimization for multi-threaded many-core applications | |
dc.contributor.author | Maity, A. | |
dc.contributor.author | Pathania, A. | |
dc.contributor.author | Mitra, T. | |
dc.date.accessioned | 2021-08-25T14:07:04Z | |
dc.date.available | 2021-08-25T14:07:04Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Maity, A., Pathania, A., Mitra, T. (2020). Pkmin: Peak power minimization for multi-threaded many-core applications. Journal of Low Power Electronics and Applications 10 (4) : 1-15. ScholarBank@NUS Repository. https://doi.org/10.3390/jlpea10040031 | |
dc.identifier.issn | 20799268 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/199305 | |
dc.description.abstract | Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework. © 2020 by the authors. | |
dc.publisher | MDPI AG | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Scopus OA2020 | |
dc.subject | Directed acyclic task graphs | |
dc.subject | Many-core | |
dc.subject | Peak-power management | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.3390/jlpea10040031 | |
dc.description.sourcetitle | Journal of Low Power Electronics and Applications | |
dc.description.volume | 10 | |
dc.description.issue | 4 | |
dc.description.page | 1-15 | |
Appears in Collections: | Staff Publications Elements |
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