• WORKSHOP

    19 y 20 de Septiembre, Bogota D.C, Colombia
    La serie de talleres sobre programación paralela y de alto desempeño, ofrecidos por la universidad Distrital Francisco José de Caldas tienen como objetivo proporcionar una introducción completa y práctica a técnicas y tecnologías de programación y optimización en computación paralela basadas en estándares y marcos abiertos con el fin de utilizar totalmente las capacidades de escalamiento…

    DETALLES DEL EVENTO E INSCRIPCIONES
  • SEMINARIO

    Seminario sobre optimización de aplicaciones MATLAB dentro del marco del HPC UD'17.

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  • AWS CLOUD SERVICES FOR RESEARCH COMPUTING AND HPC

    Researchers are increasingly bringing their HPC workloads to the AWS cloud. In this presentation, we will visit AWS capabilities, real-world examples, and best practices, to shine a light on:
    - AWS services that make scientific computing accessible to everyone – from new HPC virtual server types to personal HPC clusters in the cloud;
    - New approaches to HPC, including serverless computing;
    - How AWS works with the research community and supports science.

    DETALLES DEL EVENTO E INSCRIPCIONES
esenfrpt
Lunes, 13 Marzo 2017 09:24

enerGyPU and enerGyPhi Monitor for Power Consumption and Performance Evaluation on Nvidia Tesla GPU and Intel Xeon Phi

Escrito por John A. García H., Esteban Hernandez B., Carlos E. Montenegro., Philippe O. Navaux, Carlos J. Barrios H.
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Abstract

—The evaluation of performance and power consump-tion is a key step in the design of applications for large compu-tational systems as supercomputers and clusters (multicore andaccelerator nodes, multicore and coprocessor nodes, manycoreand accelerator nodes).

In these systems the developers must de-sign several experiments for workload characterization observingthe architectural implications when using different combinationsof computational resources such as number of GPU, number of cores for processing, number of cores for administration of GPU,number of MPI processes and thread affinity policy. It shouldalso engage factors as the clock frequency and memory usageas well select the combination of computational resources thatincreases the performance and minimizes the power consumption.This research proposes an integrated energy-aware scheme calledefficiently energetic acceleration (EEA) for large-scale scientificapplications running on heterogeneous architectures. This papershows the use of a monitoring tool with two components calledenerGyPU and enerGyPhi to recording EEA control factors inruntime on two environments: one cluster with multicore andaccelerator nodes (2-CPU/8-GPU) and one server with multiplecores and one coprocessor (2-CPU/1-MIC). These monitors allowto analyze multiple testing results under different parametercombinations to observe the EEA control factors that determinethe energy efficiency.

 Index Terms

—Energy efficiency, Energy-aware EEA schemeenerGyPU, enerGyPhi, Power capping technique, Performanceevaluation.

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