Collaborative heterogeneity-aware OS scheduler for asymmetric multicore processors

Teng Yu, Runxin Zhong, Vladimir Janjic, Pavlos Petoumenos, Jidong Zhai*, Hugh Leather, John Donald Thomson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
5 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1224-1237
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number5
Early online date16 Dec 2020
Publication statusPublished - 1 May 2021


  • Assymetric multicore processors
  • Operating system
  • Scheduling
  • Performance model
  • Energy efficiency


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