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CELERITY: advanCed modELing for scalablE distRIbuted runTime sYstems.



High-Performance Computing (HPC) plays a fundamental role in enabling scientific progress, as improvements in many areas of science critically depend on advances in computational modeling and processing power. The next major milestone for the HPC community is the transition from peta to exascale, which imposes difficult research challenges such as programming models for scientific productivity, scalable system software design, and energy efficiency. We propose the CELERITY environment to support the effective development of energy- and performance- efficient, predictably scalable and easy-to-program parallel applications targeting large-scale homogenous and heterogeneous HPC clusters. The CELERITY environment, thanks to its high-level programming model, assures high productivity by relieving the programmer of labor-intensive low-level concerns such as task partitioning and distribution -- which are particularly demanding when targeting heterogeneous distributed architectures. To provide high performance, CELERITY will combine novel static kernel analyses integrated with dynamic information provided by the runtime system, enabling modeling and predictions of parallel scalability as well as energy consumption for a given input application. The modeling will be based on advanced machine learning methodologies such as structural learning, leveraging the inner representation of the data to deliver accurate predictions. We plan to assess our system first on a small-scale heterogeneous cluster instrumented with very accurate energy measurement devices, and successively on several large-scale clusters with the available instrumentation provided by the computing infrastructures, using a broad collection of application benchmarks.


Project website:  https://celerity.github.io/

Source code: https://github.com/celerity/celerity-runtime






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