Abstract
Multicore multiprocessors use Non Uniform Memory Architecture (NUMA) to improve their scalability. However,NUMA introduces performance penalties due to remote memory accesses. Without efficiently managing data layout and thread mapping to cores, scientific applications, even if they are optimized for NUMA, may suffer performance loss. In this paper, we present an algorithm that optimizes the placement of OpenMP threads on NUMA processors. By collecting information from hardware counters and defining new metrics to capture the effects of thread placement, the algorithm reduces NUMA performance penalty by minimizing the critical path of OpenMP parallel regions and by avoiding local memory resource contention. We evaluate our algorithm with NPB benchmarks and achieve performance improvement between 8.13% and 25.68%, compared to the OS default scheduling.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
