TProf: An energy profiler for task-parallel programs
We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation...
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| Published in: | Sustainable computing informatics and systems Vol. 5; pp. 1 - 13 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.03.2015
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| ISSN: | 2210-5379 |
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| Abstract | We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS). |
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| AbstractList | We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS). |
| Author | Zakkak, Foivos S. Manousakis, Ioannis Nikolopoulos, Dimitrios S. Pratikakis, Polyvios |
| Author_xml | – sequence: 1 givenname: Ioannis surname: Manousakis fullname: Manousakis, Ioannis email: im159@cs.rutgers.edu organization: Department of Computer Science, Rutgers University, United States – sequence: 2 givenname: Foivos S. surname: Zakkak fullname: Zakkak, Foivos S. email: zakkak@ics.forth.gr organization: Institute of Computer Science, Foundation for Research and Technology, Hellas, Greece – sequence: 3 givenname: Polyvios surname: Pratikakis fullname: Pratikakis, Polyvios email: polyvios@ics.forth.gr organization: Institute of Computer Science, Foundation for Research and Technology, Hellas, Greece – sequence: 4 givenname: Dimitrios S. orcidid: 0000-0003-0217-8307 surname: Nikolopoulos fullname: Nikolopoulos, Dimitrios S. email: d.nikolopoulos@qub.ac.uk organization: School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, United Kingdom |
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| Keywords | Parallel programming models Energy profiling Task parallelism Parallel runtime systems |
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Fine grained energy accounting on smartphones with Eprof – volume: 25 start-page: 39 issue: September (5) year: 2005 ident: 10.1016/j.suscom.2014.07.004_bib0150 article-title: Long-term workload phases: duration predictions and applications to DVFS publication-title: Micro IEEE doi: 10.1109/MM.2005.93 |
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| Snippet | We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf... |
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| SubjectTerms | Energy profiling Parallel programming models Parallel runtime systems Task parallelism |
| Title | TProf: An energy profiler for task-parallel programs |
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