کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4949085 1439961 2017 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Marcher: A Heterogeneous System Supporting Energy-Aware High Performance Computing and Big Data Analytics
ترجمه فارسی عنوان
مارچر: یک سیستم ناهمگن از محاسبات با کارایی بالا با انرژی و تجزیه و تحلیل داده های بزرگ پشتیبانی می کند
کلمات کلیدی
محاسبه انرژی کارآمد با کارایی بالا، تجزیه و تحلیل داده های بزرگ اطلاعات انرژی، سیستم های قابل اندازه گیری قدرت پروفایل قدرت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Excessive energy consumption is a major constraint in designing and deploying the next generation of supercomputers. Minimizing energy consumption of high performance computing and big data applications requires novel energy-conscious technologies (both hardware and software) at multiple layers from architecture, system support, and applications. In the past decade, we have witnessed the significant progress toward developing more energy-efficient hardware and facility infrastructure. However, the energy efficiency of software has not been improved much. One obstacle that hinders the exploration of green software technologies is the lack of tools and systems that can provide accurate, fine-grained, and real-time power and energy measurement for technology evaluation and verification. Marcher, a heterogeneous high performance computing infrastructure, is built to fill the gap by providing support to research in energy-aware high performance computing and big data analytics. The Marcher system is equipped with Intel Xeon CPUs, Intel Many Integrated Cores (Xeon Phi), Nvidia GPUs, power-aware memory systems and hybrid storage with Hard Disk Drives (HDDs) and Solid State Disks (SSDs). It provides easy-to-use tools and interfaces for researchers to obtain decomposed and fine-grained power consumption data of these primary computing components. This paper presents the design of the Marcher system and demonstrates the usage of Marcher power measurement tools to obtain detailed power consumption data in various research projects.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Big Data Research - Volume 8, July 2017, Pages 27-38
نویسندگان
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