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This work describes workflow modeling using Workflownets and Generalized Stochastic Petri Nets (GSPN)

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for their insightful feedback, comments and suggestions in improving the quality of this dissertation. I am proud to have such wonderful and knowledgeable people serving on my dissertation committee.

The objective of this work is to evaluate the viability of implementing workload-aware dynamic power management schemes on a many-core platform, aiming at reducing power consumption for high performance computing (HPC) application. Two approaches were proposed to achieve the desired target. First approach is an off-line scheduling scheme where core voltage and frequency are set up beforehand based on the workload characterization of the application. The second approach is an on-line scheduling scheme, where core voltage and frequency are controlled based on a workload detection algorithm. Experiments were conducted using the 48-core Intel Single-chip Cloud Computer (SCC), running a parallelized Fire Spread Monte Carlo Simulation program. Both schemes were compared against a performance-driven, but non-power-aware management scheme.The results indicate that our schemes are able to reduce the power consumption up to 29% with mild impact on the system performance.