In this work we introduce a history-aware, resourcebased dynamic (or simply HARD) scheduler for heterogeneous CMPs. HARD relies on recording application resource utilization and throughput to adaptively change cores for applications during runtime. We show that HARD can be configured to achieve both performance and power improvements. We compare HARD to a complexity-based static scheduler and show that HARD outperforms this alternative.

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In present study, in order to improve the performance and reduce the amount of power which is dissipated in heterogeneous multicore processors, the ability of detecting the program execution phases is investigated. The program's execution intervals have been classified in different phases based on their throughput and the utilization of the cores. The results of implementing the phase detection technique are investigated on a single core processor and also on a multi-core processor. To minimize the profiling overhead, an algorithm for the dynamic adjustment of the profiling intervals is presented. It is based on the behavior of the program and reduces the profiling overhead more than three fold. The results are obtained from executing multiprocessor benchmarks on a given processor. In order to show the program phases clearly, throughput and utilization of execution intervals are presented on a scatter plot. The results are presented for both fixed and variable intervals.

The goal of this work is to revisit GPU design and introduce a fast, low-cost and effective approach to optimize resource allocation in future GPUs. We have achieved this goal by using the Plackett-Burman methodology to explore the design space efficiently. We further formulate the design exploration problem as that of a constraint optimization. Our approach produces the optimum configuration in 84% of the cases, and in case that it does not, it produces the second optimal case with a performance penalty of less than 3.5%. Also, our method reduces the number of explorations one needs to perform by as much as 78%.