Scheduling opportunities for asymmetrically reliable caches

Sanem Arslan, Haluk Rahmi Topcuoglu, Mahmut Taylan Kandemir, Oguz Tosun

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Modern systems become more vulnerable to soft errors with technology scaling. Providing fault tolerance strategies on all structures in a system may lead to high energy consumption. Our framework with asymmetrically reliable caches with at least one protected core and several unprotected cores dynamically assigns the software threads executing critical code fragments to the protected core(s) with the FCFS-based algorithm. The framework can provide good reliability, performance, and power consumption trade-offs compared with the fully protected and unprotected systems. However, FCFS-based scheduling algorithm may degrade the system performance and unfairly slow down applications for some workloads. In this paper, a set of scheduling algorithms is proposed to improve both the system performance and fairness perspectives. Various static priority techniques that require preliminary information about the applications (such as their execution order, cache usage, number of requests sent to the protected core(s), and total burst time spent on the protected core(s)) are implemented and evaluated. On the other hand, dynamic priority techniques that target to equalize the total time spent of applications on the protected core(s) or the progress of the applications’ requests are presented. Extensive evaluations using multi-application workloads validate significant improvements of our static and dynamic priority scheduling techniques on system performance and fairness over the FCFS algorithm.

Original languageEnglish (US)
Pages (from-to)134-151
Number of pages18
JournalJournal of Parallel and Distributed Computing
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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