Automatic Tuning on Many-Core Platform for Energy Efficiency via Support Vector Machine Enhanced Differential Evolution


Zhiliu Yang
Zachary I. Rauen
Chen Liu


The modern era of computing involves increasing the core count of the processor, which in turn increases the energy usage of the processor. How to identify the most energy-efficient way of running a multiple-program workload on a many-core processor while still maintaining a satisfactory performance level is always a challenge. Automatic tuning on the voltage and frequency level of a many-core processor is an effective method to aid solving this dilemma. The metrics we focus on optimizing are energy usage and energy-delay product (EDP). To this end, we propose SVM-JADE, a machine learning enhanced version of an adaptive differential evolution algorithm (JADE). We monitor the energy and EDP values of different voltage and frequency combinations of the cores, or power islands, as the algorithm evolves through generations. By adding a well-tuned support vector machine (SVM) to JADE, creating SVM-JADE, we are able to achieve energy-aware computing on many-core platform when running multiple-program workloads. Our experimental results show that our algorithm can further improve the energy by 8.3% and further improve EDP by 7.7% than JADE. Besides, in both EDP-based and energy-based fitness SVM-JADE converges faster than JADE. Parallel tree skeletons are basic computational patterns that can be used to develop parallel programs for manipulating trees. In this paper, we propose an efficient implementation of parallel tree skeletons on distributed-memory parallel computers. In our implementation, we divide a binary tree to segments based on the idea of m-bridges with high locality, and represent local segments as serialized arrays for high sequential performance. We furthermore develop a cost model for our implementation of parallel tree skeletons. We confirm the efficacy of our implementation with several experiments.


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