Adaptive Performance Optimization under Power Constraint in Multi-thread Applications.

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 aims at setting the best combination of thread-level parallelism and CPU-core power state depending on the workload profile of the multi-threaded application. We evaluate our proposal by relying on different benchmarks, configured to use different thread synchronization methods. This makes the set of tested configurations wide and increases the representativeness of the experimental outcomes.