Evaluating High-Performance Computing based on Relative Productivity Indicator

被引：0
|
作者：
Wang, Jie [1 ]
Zeng, Yu [2 ]
Lv, Huiying [1 ]
Lin, Yun [1 ]
机构：
[1] Capital Normal Univ, Sch Management, Beijing 100089, Peoples R China
[2] Beijing Comp Ctr, Beijing 100094, Peoples R China
来源：
2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2013年
关键词：
high-performance computing; cluster performance evaluation; relative productivity;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Effective high-performance computing evaluation can promote the development of high-performance cluster systems tremendously. In this paper, we propose a reasonable and easy mechanism named RPI (relative productivity indicator) to evaluate high-performance cluster systems. RPI considers many factors comprehensively, such as system purchasing cost, operation cost, performance of key application, difficulty of programming and the complexity of management. RPI avoids the problem of different dimension of various parameters caused by direct measurement effectively. We also use a real high-performance cluster Dawning 5000A to prove the effectiveness of the RPI.
引用
收藏
页码：1809 / 1813
页数：5
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