This paper proposes a novel technique for power-performance trade-off based on profile-driven code execution. Specifically, we show that there is an optimal level of parallelism for energy consumption and propose a compiler-assisted technique for code annotation that can be used at run-time to adaptively trade-off power and performance. As shown by experimental results, our approach is up to 23% better than clock throttling and is as efficient as voltage scaling (up to 10% better in some cases). The technique proposed in this paper can be used by an ACPI-compliant power manager for prolonging battery life or as a passive cooling feature for thermal management. Author(s) Marculescu, D. Carnegie Mellon Univ., Pittsburgh, PA, USA