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Reliable and energy-aware mapping of streaming series-parallel applications onto hierarchical platforms

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Abstract

Streaming applications come from various application fields such as physics, and many can be represented as a series-parallel dependence graph. We aim at minimizing the energy consumption of such applications when executed on a hierarchical platform, by proposing novel mapping strategies. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it. In this paper, we propose a structure rule to partition the series-parallel applications, and we prove that the optimization problem is NP-complete. We are able to derive a dynamic-programming algorithm for the special case of linear chains, which provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics performance is compared to a baseline solution, where each task is executed at maximum speed. Simulations demonstrate that significant energy savings can be obtained.
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Dates and versions

hal-03024594 , version 1 (25-11-2020)

Identifiers

  • HAL Id : hal-03024594 , version 1

Cite

Changjiang Gou, Anne Benoit, Mingsong Chen, Loris Marchal, Tongquan Wei. Reliable and energy-aware mapping of streaming series-parallel applications onto hierarchical platforms. SBAC-PAD 2020 - IEEE 32nd International Symposium on Computer Architecture and High Performance Computing, Sep 2020, Porto, Portugal. pp.1-11. ⟨hal-03024594⟩
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