An Effective Speedup Metric Considering I/O
Constraint in Large-scale Parallel Computer
SystemsGuilin Cai*, Wei Hu*, Guangming Liu*,**, Qiong Li*, Xiaofeng Wang*, Wenrui Dong**College of Computer, National University of Defense Technology, Changsha, China**National Supercomputer Center in Tianjin, Tianjin, China
cc_cai@163.com, { huwei, liugm}@nscc-tj.gov.cn, qiong_joan_li@yahoo.com.cn, xf_wang@nudt.edu.cn,
dongwr@nscc-tj.gov.cn

Abstract—With supercomputer system scaling up, the performance gap between compute and storage system increases
dramatically. The traditional speedup only measures the performance of compute system. In this paper, we firstly propose thespeedup metric taking into account the I/O constraint. The new metric unifies the computing and I/O performance, and evaluatespractical speedup of parallel application under the limitation of I/O system. Furthermore, this paper classifies and analyzes existing
parallel systems according to the proposed speedup metric, and makes suggestions on system design and application optimization.
Based on the storage speedup, we also generalize these results into a general storage speedup by considering not only speedup but
also costup. Finally, we provide the analysis of these new speedup metrics by case studies. The storage speedup reflects the degree ofparallel application scalability affected by performance of storage system. The results indicate that the proposed speedups forparallel applications are effective metrics.
Keyword—storage speedup, general storage speedup, scalability, system classification
Guilin Cai is a doctor student in the College of Computer at National University of Defense Technology in China. She received the B.S.
and M.S. degrees in computer science from National University of Defense Technology in 2005 and 2010, respectively. Her main researchinterests include high performance computing, cyber security and proactive defense.

Wei Hu received the B.S. degree from PLA University of Science and Technology, China, in 2004, and the M.S. degree from National
University of Defense Technology, China, in 2010. He currently pursues the Ph.D. degree in the College of Computer, National
University of Defense Technology, Changsha, China. His research interests include high performance computing and machine learning.

Guangming Liu received the B.S. and M.S. degrees from National University of Defense Technology, China, in 1980 and 1986
respectively. He is now a professor in the College of Computer, National University of Defense Technology. His research interests
include high performance computing, massive storage and cloud computing.

Qiong Li received the B.S., M.S. and Ph.D. degrees from National University of Defense Technology, China, in 1993, 1996 and 2010,
respectively. She is now a professor in the College of Computer, National University of Defense Technology. Her research interests
include high performance computing and massive storage.

Xiaofeng Wang has been working as an assistant professor in the College of Computer at National University of Defense Technologyin China. He received the B.S., M.S. and Ph.D. degrees in computer science from National University of Defense Technology in 2004,
2006 and 2009 respectively. His research interests include trustworthy networks and systems, applied cryptography, network security.

Wenrui Dong received the B.S. and M.S. degrees from National University of Defense Technology, China, in 2009 and 2011,
respectively. He currently pursues the Ph.D. degree in the College of Computer, National University of Defense Technology. His
research interests include high performance computing and massive storage.