Energy Implications of Common Operations in Resource-Intensive Java-Based Scientific Applications

Mateos, Cristian; Rodríguez, Alejandro Damián; Longo, Mathias; Zunino, Alejandro

doi:10.1007/978-3-319-31232-3_69

Cited by 1 publication

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“…We limit the scope of our research to Java, which is useful for developing HPC applications and middlewares [41] because of its "write once, run anywhere" philosophy. This work is based on an earlier conference version published in [25], but it introduces several pertinent enhancements, namely:…”

Section: Introductionmentioning

confidence: 99%

Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

et al. 2019

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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%.

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Section: Introductionmentioning

confidence: 99%