Titlebook: High Performance Computing; 31st International C Julian M. Kunkel,Pavan Balaji,Jack Dongarra Conference proceedings 2016 Springer Internati

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TiDA: High-Level Programming Abstractions for Data Locality Managementlity manually is not a trivial task and also complicates programming. Tiling is a well-known approach that provides both data locality and parallelism in an application. However, there is no standard programming construct to express tiling at the application level. We have developed a multicore prog

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SPRITE: A Fast Parallel SNP Detection Pipelinepipeline for next-generation sequencing data uses several software tools, including tools for read alignment, processing alignment output, and SNP identification. We target end-to-end scalability and I/O efficiency in . by merging tools in this pipeline and eliminating redundancies. For a benchmark

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Predictive Modeling for Job Power Consumption in HPC Systemsut this will require a significant improvement in terms of energy efficiency. In this domain, power-capping can significant increase the final energy-efficiency by cutting cooling effort and worst-case design margins. A key aspect for an optimal implementation of power capping is the ability to esti

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Towards Machine Learning on the Automata Processor In this paper, we discuss the implementation of such a method, namely Random Forests, as the first machine learning algorithm to be executed on the Automata Processor (AP). The AP is an upcoming reconfigurable co-processor accelerator which supports the execution of numerous automata in parallel ag

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