@phdthesis{saha_multi-objective_2016,
	title = {A {Multi}-objective {Autotuning} {Framework} {For} {The} {Java} {Virtual} {Machine}},
	url = {https://digital.library.txstate.edu/handle/10877/6096},
	abstract = {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.},
	urldate = {2016-07-05},
	school = {Texas State University},
	author = {Saha, Shuvabrata},
	month = apr,
	year = {2016},
}

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