Palirria: Accurate on-line parallelism estimation for adaptive work-stealing
2014 (English)In: PMAM'14 Proceedings of Programming Models and Applications on Multicores and Manycore, ACM Press, 2014, 120-130 p.Conference paper (Refereed)
We present Palirria, a self-adapting work-stealing scheduling method for nested fork/join parallelism that can be used to estimate the number of utilizable workers and self-adapt accordingly. The estimation mechanism is optimized for accuracy, minimizing the requested resources without degrading performance. We implemented Palirria for both the Linux and Barrelfish operating systems and evaluated it on two platforms: a 48-core NUMA multiprocessor and a simulated 32-core system. Compared to state-of-the-art, we observed higher accuracy in estimating resource requirements. This leads to improved resource utilization and performance on par or better to executing with fixed resource allotments.
Place, publisher, year, edition, pages
ACM Press, 2014. 120-130 p.
parallel, workload, runtime, task, adaptive, resource management, load balancing, work-stealing
IdentifiersURN: urn:nbn:se:kth:diva-128184DOI: 10.1145/2560683.2560687ScopusID: 2-s2.0-84897716570ISBN: 978-1-4503-2657-5OAI: oai:DiVA.org:kth-128184DiVA: diva2:647141
2014 International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2014; Orlando, FL; United States; 15 February 2014 through 15 February 2014
QC 201405202013-09-102013-09-102014-05-20Bibliographically approved