AKULA: a toolset for experimenting and developing thread placement algorithms on multicore systems

  • Authors:

  • Sergey Zhuravlev;Sergey Blagodurov;Alexandra Fedorova

  • Affiliations:

  • Simon Fraser University, Vancouver, BC, Canada;Simon Fraser University, Vancouver, BC, Canada;Simon Fraser University, Vancouver, BC, Canada

  • Venue:
  • Proceedings of the 19th international conference on Parallel architectures and compilation techniques
  • Year:
  • 2010

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Abstract

Multicore processors have become commonplace in both desk-top and servers. A serious challenge with multicore processors is that cores share on and o chip resources such as caches, memory buses, and memory controllers. Competition for these shared resources between threads running on different cores can result in severe and unpredictable performance degradations. It has been shown in previous work that the OS scheduler can be made shared-resource-aware and can greatly reduce the negative e ects of resource contention. The search space of potential scheduling algorithms is huge considering the diversity of available multicore architectures, an almost infinite set of potential workloads, and a variety of conflicting performance goals. We believe the two biggest obstacles to developing new scheduling algorithms are the difficulty of implementation and the duration of testing. We address both of these challenges with our toolset AKULA which we introduce in this paper. AKULA provides an API that allows developers to implement and debug scheduling algorithms easily and quickly without the need to modify the kernel or use system calls. AKULA also provides a rapid evaluation module, based on a novel evaluation technique also introduced in this paper, which allows the created scheduling algorithm to be tested on a wide variety of work-loads in just a fraction of the time testing on real hardware would take. AKULA also facilitates running scheduling algorithms created with its API on real machines without the need for additional modifications. We use AKULA to develop and evaluate a variety of different contention-aware scheduling algorithms. We use the rapid evaluation module to test our algorithms on thousands of workloads and assess their scalability to futuristic massively multicore machines.