Performance Studies of Commercial Workloads on a Multi-core System

The multi-threaded nature of many commercial applications makes them seemingly a good fit with the increasing number of available multi-core architectures. This paper presents our performance studies of a collection of commercial workloads on a multi-core system that is designed for total throughput. The selected workloads include full operational applications such as SAP-SD and IBM Trade, and popular synthetic benchmarks such as SPECjbb2005, SPEC SDET, Dbench, and Tbench. To evaluate the performance scalability and the thread-placement sensitivity, we monitor the application throughput, processor performance, and the memory subsystem of 8, 16, 24, and 32 hardware threads with (a) increasing number of cores and (b) increasing number of threads per core. We observe that these workloads scale close to linearly (with efficiencies ranging from 86% to 99%) with increasing number of cores. For scaling with hardware-threads per core, the efficiencies are between 50% and 70%. Furthermore, among other observations, our data show that the ability of hiding long latency memory operations (i.e. L2 misses) in a multi-core system enables the performance scaling.