Adaptive Performance Optimization under Power Constraint in Multi-thread Applications with Diverse Scalability

TL;DR

This paper presents an adaptive method to optimize multi-threaded application performance under power constraints by dynamically tuning thread parallelism and CPU power states, considering diverse scalability behaviors.

Contribution

It introduces a linear-time adaptive technique for selecting optimal thread and power configurations tailored to specific workloads, addressing limitations of existing power capping approaches.

Findings

Improves application performance under power caps compared to state-of-the-art methods.

Effectively adapts to different synchronization methods and workload profiles.

Reduces tuning overhead with linear-time optimization.

Abstract

In modern data centers, energy usage represents one of the major factors affecting operational costs. Power capping is a technique that limits the power consumption of individual systems, which allows reducing the overall power demand at both cluster and data center levels. However, literature power capping approaches do not fit well the nature of important applications based on first-class multi-thread technology. For these applications performance may not grow linearly as a function of the thread-level parallelism because of the need for thread synchronization while accessing shared resources, such as shared data. In this paper we consider the problem of maximizing the application performance under a power cap by dynamically tuning the thread-level parallelism and the power state of the CPU-cores. Based on experimental observations, we design an adaptive technique that selects in linear time the optimal combination of thread-level parallelism and CPU-core power state for the specific workload profile of the multi-threaded application. We evaluate our proposal by relying on different benchmarks, configured to use different thread synchronization methods, and compare its effectiveness to different state-of-the-art techniques.