Collaborative heterogeneity-aware OS scheduler for asymmetric multicore processors
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Asymmetric multicore processors (AMP) offer multiple types of cores under the same programming interface. Extracting the full potential of AMPs requires intelligent scheduling decisions, matching each thread with the right kind of core, the core that will maximize performance or minimize wasted energy for this thread. Existing OS schedulers are not up to this task. While they may handle certain aspects of asymmetry in the system, none can handle all runtime factors affecting AMPs for the general case of multi-threaded multi-programmed workloads. We address this problem by introducing COLAB, a general purpose asymmetry-aware scheduler targeting multi-threaded multi-programmed workloads. It estimates the performance and power of each thread on each type of core and identifies communication patterns and bottleneck threads. With this information, the scheduler makes coordinated core assignment and thread selection decisions that still provide each application its fair share of the processor’s time. We evaluate our approach using both the GEM5 simulator on four distinct big.LITTLE configurations and a development board with ARM Cortex-A73/A53 processors and mixed workloads composed of PARSEC and SPLASH2 benchmarks. Compared to the state-of-the art Linux CFS and AMP-aware schedulers, we demonstrate performance gains of up to 25% and 5% to 15% on average,together with an average 5% energy saving depending on the hardware setup.
Yu , T , Zhong , R , Janjic , V , Petoumenos , P , Zhai , J , Leather , H & Thomson , J D 2021 , ' Collaborative heterogeneity-aware OS scheduler for asymmetric multicore processors ' , IEEE Transactions on Parallel and Distributed Systems , vol. 32 , no. 5 , pp. 1224-1237 . https://doi.org/10.1109/TPDS.2020.3045279
IEEE Transactions on Parallel and Distributed Systems
Copyright © 2020 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/TPDS.2020.3045279.
DescriptionFunding: This work is supported in part by the China Postdoctoral Science Foundation (Grant No. 2020TQ0169), the ShuiMu Tsinghua Scholar fellowship (2019SM131), National Key R&D Program of China (2020AAA0105200), National Natural Science Foundation of China (U20A20226), Beijing Natural Science Foundation (4202031), Beijing Academy of Artificial Intelligence BAAI), the UK EPSRC grants Discovery: Pattern Discovery and Program Shaping for Manycore Systems (EP/P020631/1). This work is also supported by the Royal Academy of Engineering under the Research Fellowship scheme.
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