Thread Progress Equalization: Dynamically Adaptive Power and Performance Optimization of Multi-threaded Applications

TL;DR

This paper introduces Thread Progress Equalization (TPEq), a run-time mechanism that dynamically optimizes power and performance in multi-core processors for multithreaded applications without source code modifications.

Contribution

TPEq uniquely addresses inter-thread heterogeneity, finds optimal core configurations efficiently, and requires no changes to user-level code.

Findings

TPEq outperforms existing methods by up to 23% in power/performance optimization.

It effectively manages inter-thread heterogeneity in multithreaded applications.

The algorithm operates in polynomial time relative to cores and configurations.

Abstract

Dynamically adaptive multi-core architectures have been proposed as an effective solution to optimize performance for peak power constrained processors. In processors, the micro-architectural parameters or voltage/frequency of each core to be changed at run-time, thus providing a range of power/performance operating points for each core. In this paper, we propose Thread Progress Equalization (TPEq), a run-time mechanism for power constrained performance maximization of multithreaded applications running on dynamically adaptive multicore processors. Compared to existing approaches, TPEq (i) identifies and addresses two primary sources of inter-thread heterogeneity in multithreaded applications, (ii) determines the optimal core configurations in polynomial time with respect to the number of cores and configurations, and (iii) requires no modifications in the user-level source code. Our experimental evaluations demonstrate that TPEq outperforms state-of-the-art run-time power/performance optimization techniques proposed in literature for dynamically adaptive multicores by up to 23%.