Data locality of work stealing

Abstract

This paper studies the data locality of the work-stealing scheduling algorithm on hardware-controlled shared-memory machines. We present lower and upper bounds on the number of cache misses using work stealing, and introduce a locality-guided work-stealing algorithm along with experimental validation. As a lower bound, we show that there is a family of multi-threaded computations Gn each member of which requires θ(n) total instructions (work), for which when using work-stealing the number of cache misses on one processor is constant, while even on two processors the total number of cache misses is Ω(n). This implies that for general computations there is no useful bound relating multiprocessor to uninprocessor cache misses. For nested-parallel computations, however, we show that on P processors the expected additional number of cache misses beyond those on a single processor is bounded by O(C[m/s]PT∞), where m is the execution time of an instruction incurring a cache miss, s is the steal time, C is the size of cache, and T∞ is the number of nodes on the longest chain of dependences. Based on this we give strong bounds on the total running time of nested-parallel computations using work stealing. For the second part of our results, we present a locality-guided work stealing algorithm that improves the data locality of multi-threaded computations by allowing a thread to have an affinity for a processor. Our initial experiments on iterative data-parallel applications show that the algorithm matches the performance of static-partitioning under traditional work loads but improves the performance up to 50% over static partitioning under multiprogrammed work loads. Furthermore, the locality-guided work stealing improves the performance of work-stealing up to 80%.