Data Aware Scheduling for Multi-threaded Applications on SMP Machines

Extensive use of multi-threaded applications that run on SMP machines justifies modifications in thread scheduling algorithms to consider threads' characteristics in order to improve performance. Current schedulers (e.g. in Linux, AIX) avoid migrating tasks between CPUs unless absolutely necessary. Unwarranted data cache misses occur when tasks that share data run on different CPUs, or are far apart time-wise on the same CPU. This work presents an extension to the Linux scheduler that exploits inter-task data relations to reduce data cache misses in multi-threaded applications running on SMP platforms, thus improving runtime, memory throughput, and energy consumption. Our approach schedules the tasks to the CPU that holds the relevant data rather than to the one with highest affinity. We observed improvements in CPU time and throughput on several benchmarks. For the Chat benchmark the improvement in CPU time and cache misses is over 30% on average.

Dr. Pinter was a research specialist at MIT before receiving her Ph.D. in computer science from Boston University. Following her Ph. D., she joined the faculty of the Electrical Engineering Department of Technion (Israel), remaining for twelve years before joining the IBM research lab in Haifa as member of research staff. She recently left IBM to become CTO of Rascal Software Security. She is also an adjunct faculty member of the Department of Computer Science, Haifa University, supervising graduate student research.