Nicolas Melot
Christoph Kessler
ABSTRACT
We demonstrate how static, energy-efficient, compiler-generated schedules for independent, parallelizable tasks on parallel machines can be improved by modeling idle power. We assume that the static power consumption of a core comprises a notable fraction of the core's total power, which is more and more often the case. The improvement is achieved by optimally packing cores when deciding about core allocation, mapping and DVFS for each task so that all unused cores can be switched off and overall energy usage is minimized. We evaluate our proposal with a benchmark suite of task collections, and compare the resulting schedules with an optimal scheduler that does not take idle power and core switch-off into account. We find that we can reduce energy consumption by 66% for mostly sequential tasks on many cores and by up to 91% for a realistic multicore processor model.
- M. I. Gordon, W. Thies, and S. Amarasinghe. Exploiting Coarse-grained Task, Data, and Pipeline Parallelism in Stream Programs. In Proc. 12th Int. Conf. on Architectural Support for Programming Languages and Operating Systems, ASPLOS XII, pages 151--162. ACM, 2006. Google Scholar
Digital Library
- N. Melot, C. Kessler, and J. Keller. Improving Energy-Efficiency of Static Schedules by Core Consolidation and Switching Off Unused Cores. In Proc. of Int. Conf. on Parallel Computing (ParCO 2015), Edinburgh, UK, September 2015. IOS Press, to appear 2016.Google Scholar
- N. Melot, C. Kessler, J. Keller, and P. Eitschberger. Fast Crown Scheduling Heuristics for Energy-Efficient Mapping and Scaling of Moldable Streaming Tasks on Many-core Systems. ACM Trans. Archit. Code Optim., 11(4): 62:1--62:24, Jan. 2015. ISSN 1544-3566. Google ScholarDigital Library
- H. Xu, F. Kong, and Q. Deng. Energy Minimizing for Parallel Real-Time Tasks Based on Level-Packing. In 18th Int. Conf. on Emb. and Real-Time Comput. Syst. and Appl. (RTCSA), pages 98--103, Aug 2012. Google ScholarDigital Library
Index Terms
- Energy-Optimized Static Scheduling for Many-Cores with Task Parallelization, DVFS and Core Consolidation
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