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

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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Submitted on : Thursday, July 2, 2020 - 11:43:54 AM
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Changjiang Gou, Anne Benoit, Mingsong Chen, Loris Marchal, Tongquan Wei. Reliable and energy-aware mapping of streaming series-parallel applications onto hierarchical platforms. [Research Report] RR-9346, INRIA. 2020. ⟨hal-02859980v2⟩

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