Evaluating high-performance computing based on relative productivity indicator
ICNC（2013）
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.
更多查看译文
关键词
parallel processing,rpi mechanism,cluster performance evaluation,management complexity,system purchasing cost,dawning 5000a,application performance,high-performance computing,relative productivity indicator,high-performance computing evaluation,high-performance cluster system evaluation,programming difficulty,performance evaluation,computer purchase,relative productivity,operation cost,benchmark testing,productivity,economic indicators,high performance computing,programming
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要