Optimizing SMT Processors for High Single-Thread Performance

Simultaneous Multithreading (SMT) processors achieve high processor throughput at the expense of single-thread performance. This paper investigates resource allocation policies for SMT processors that preserve, as much as possible, the single-thread performance of designated “foreground” threads, while still permitting other “background” threads to share resources. Since background threads on such an SMT machine have a near-zero performance impact on foreground threads, we refer to the background threads as transparent threads. Transparent threads are ideal for performing low-priority or non-critical computations, with applications in process scheduling, subordinate multithreading, and on-line performance monitoring. To realize transparent threads, we propose three mechanisms for maintaining the transparency of background threads: slot prioritization, background thread instruction-window partitioning, and background thread flushing. In addition, we propose three mechanisms to boost background thread performance without sacrificing transparency: aggressive fetch partitioning, foreground thread instruction-window partitioning, and foreground thread flushing. We implement our mechanisms on a detailed simulator of an SMT processor, and evaluate them using 8 benchmarks, including 7 from the SPEC CPU2000 suite. Our results show when cache and branch predictor interference are factored out, background threads introduce less than 1% performance degradation on the foreground thread. Furthermore, maintaining the transparency of background threads reduces their throughput by only 23% relative to an equal priority scheme. To demonstrate the usefulness of transparent threads, we study Transparent Software Prefetching (TSP), an implementation of software data prefetching using transparent threads. Due to its near-zero overhead, TSP enables prefetch instrumentation for all loads in a program, eliminating the need for profiling. TSP, without any profile information, achieves a 9.52% gain across 6 SPEC benchmarks, whereas conventional software prefetching guided by cache-miss profiles increases performance by only 2.47%.