(Mis)understanding the NUMA memory system performance of multithreaded workloads

Abstract

An important aspect of workload characterization is understanding memory system performance (i.e., understanding a workload's interaction with the memory system). On systems with a non-uniform memory architecture (NUMA) the performance critically depends on the distribution of data and computations. The actual memory access patterns have a large influence on performance on systems with aggressive prefetcher units. This paper describes an analysis of the memory system performance of multithreaded programs and shows that some programs are (unintentionally) structured so that they use the memory system of today's NUMA-multicores inefficiently: Programs exhibit program-level data sharing, a performance-limiting factor that makes data and computation distribution in NUMA systems difficult. Moreover, many programs have irregular memory access patterns that are hard to predict by processor prefetcher units. The memory system performance as observed for a given program on a specific platform depends also on many algorithm and implementation decisions. The paper shows that a set of simple algorithmic changes coupled with commonly available OS functionality suffice to eliminate data sharing and to regularize the memory access patterns for a subset of the PARSEC parallel benchmarks. These simple source-level changes result in performance improvements of up to 3.1X, but more importantly, they lead to a fairer and more accurate performance evaluation on NUMA-multicore systems. They also illustrate the importance of carefully considering all details of algorithms and architectures to avoid drawing incorrect conclusions.