A Multi-objective Autotuning Framework For The Java Virtual Machine
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Due to inherent limitations in performance, Java was not considered a suitable platform for for scalable high-performance computing (HPC) for a long time. The scenario is changing because of the development of frameworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in usage, achieving high performance with Java is not trivial. High performance in Java relies on libraries providing explicit threads or relying on runnable-like interfaces for distributed programming. In this thesis, we develop an autotuning framework for JVM that manages multiple objective functions including execution time, power consumption, energy and perfomance-per-watt. The framework searches the combined space of JIT optimization sequences and different classes of JVM runtime parameters. To discover good configurations more quickly, the framework implements novel heuristic search algorithms. To reduce the size of the search space machine-learning based pruning techniques are used. Evaluation on recommender system workloads show that significant improvements in both performance and power can be gained by fine-tuning JVM runitme parameters.