We present a scalable Dynamic Power Management (DPM) scheme where malleable applications may change their degree of parallelism at run time depending upon the workload and performance constraints. We employ a per-application predictive power manager that autonomously controls the power states of the cores with the goal of energy efficiency. Furthermore, our DPM allows the applications to lend their idle cores for a short time period to expedite other critical applications. In this way, it allows for application-level scalability, while aiming at the overall system energy optimization. Compared to state-of-the-art centralized and distributed power management approaches, we achieve up to 58 percent (average ≈15-20 percent) ED2P reduction.

We present a self-adaptive, hybrid Dynamic Power Management (DPM) scheme for many-core systems that targets concurrently executing applications with what we call “expanding” and “shrinking” resource allocations as, for example, in [13]–[15] [27]. To avoid frequent allocation and de-allocation, it enables applications to temporarily reserve their resources and to perform local power management decisions. The expand-to-shrink time periods and resource demands are predicted on-the-fly based on the application-specific knowledge and the monitored system information. Experimental results demonstrate up to 15%-40% Energy-Delay2Product reduction of our scheme compared to state-of-the-art power management schemes like [4][8]. Self-adaptive local power-management decisions make our scheme scalable for large-scaled many-core systems as illustrated by numerous experiments.

This paper introduces a scalable hardware and software platform applicable for demonstrating the benefits of the invasive computing paradigm. The hardware architecture consists of a heterogeneous, tile-based manycore structure while the software architecture comprises a multi-agent management layer underpinned by distributed runtime and OS services. The necessity for invasive-specific hardware assist functions is analytically shown and their integration into the overall manycore environment is described.