Geodesic
Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings
Computer Science and Information Systems, Tome 16 (2019) no. 2.

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In-silico research has grown considerably. Today’s scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.
Keywords: Energy, Scientific application, Java, Micro-benchmarks, Code refactoring 
@article{CSIS_2019_16_2_a10,
     author = {Mathias Longo and Ana Rodriguez and Cristian Mateos and Alejandro Zunino},
     title = {Reducing energy usage in resource-intensive {Java-based} scientific applications via micro-benchmark based code refactorings},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {16},
     number = {2},
     year = {2019},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2019_16_2_a10/}
}
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Mathias Longo; Ana Rodriguez; Cristian Mateos; Alejandro Zunino. Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings. Computer Science and Information Systems, Tome 16 (2019) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2019_16_2_a10/