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Kernel-to-User-Mode Transition-Aware Hardware Scheduling.

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Bibliographic Details
Title: Kernel-to-User-Mode Transition-Aware Hardware Scheduling.
Authors: Markovic, Nikola1, Nemirovsky, Daniel1, Unsal, Osman1, Valero, Mateo1, Cristal, Adrian1
Source: IEEE Micro. Jul2015, Vol. 35 Issue 4, p37-47. 11p.
Subjects: Parallelism (Linguistics), Multicore processors, Computer operating systems, Motherboards, Computer input-output equipment, Linux operating systems
Abstract: As thread-level parallelism in applications has continued to expand, so has research in chip multicore processors. More and more applications are becoming multithreaded, which should lead to a growing number of threads executing on a machine. Consequentially, the operating system will require increasingly larger amounts of CPU time to schedule these threads efficiently. Instead of perpetuating the trend of performing more-complex thread scheduling in the OS, the authors propose a scheduling mechanism that can be efficiently implemented in hardware as well. Their approach of identifying multithreaded application bottlenecks such as thread synchronization sections complements the fairness-aware scheduler method. It achieves average speedup of 11.1 and 30 percent (geometric mean) compared to the state-of-the-art Fairness-Aware Scheduler and Linux OS scheduler, respectively, while being 8 percent slower compared to the state-of-the-art bottleneck identification techniques. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
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Abstract:As thread-level parallelism in applications has continued to expand, so has research in chip multicore processors. More and more applications are becoming multithreaded, which should lead to a growing number of threads executing on a machine. Consequentially, the operating system will require increasingly larger amounts of CPU time to schedule these threads efficiently. Instead of perpetuating the trend of performing more-complex thread scheduling in the OS, the authors propose a scheduling mechanism that can be efficiently implemented in hardware as well. Their approach of identifying multithreaded application bottlenecks such as thread synchronization sections complements the fairness-aware scheduler method. It achieves average speedup of 11.1 and 30 percent (geometric mean) compared to the state-of-the-art Fairness-Aware Scheduler and Linux OS scheduler, respectively, while being 8 percent slower compared to the state-of-the-art bottleneck identification techniques. [ABSTRACT FROM PUBLISHER]
ISSN:02721732
DOI:10.1109/MM.2015.80