DeepComp: towards a balanced system design for
high performance computer systems

doi:10.1007/s11704-010-0150-z

Front. Comput. Sci.

2010, Vol. 4

Issue (4) : 475-479 https://doi.org/10.1007/s11704-010-0150-z

Research articles

DeepComp: towards a balanced system design for high performance computer systems

Mingfa ZHU¹,Limin XIAO¹,Li RUAN²,Qinfen HAO²,

1.State Key Laboratory of Software Development Environment, Beijing 100191, China；School of Computer Science and Engineering, Beihang University, Beijing 100191, China; 2.School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

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Abstract Today, cluster-based computing is the mainstream architecture for high end computer systems. Balanced system design is critical for large scale cluster systems to achieve high efficiency. This paper addresses the practice on DeepComp high end computer systems toward a balanced system design. Methodologies of designing balanced large scale cluster systems are given. A method for balancing central processing unit (CPU) and memory hierarchy is addressed. For balancing computing nodes and I/O systems, two approaches are given: maximum bandwidth criterion and maximum number of computing nodes which can concurrently access I/O systems. Experiences of Lenovo high end cluster systems show that above methods are effective. Lenovo strategies toward a balanced system design for both peta and 10 peta scale high productivity computing systems (HPCSs).

Keywords high performance computer systems (HPCs) high productivity computing systems (HPCSs) cluster balanced system design

Issue Date: 05 December 2010

Cite this article:

Mingfa ZHU,Li RUAN,Limin XIAO, et al. DeepComp: towards a balanced system design for high performance computer systems[J]. Front. Comput. Sci., 2010, 4(4): 475-479.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-010-0150-z
https://academic.hep.com.cn/fcs/EN/Y2010/V4/I4/475

	Lenovo Group. TechnicalReport of Lenovo DeepComp 6800. Beijing, 2003
	Lenovo Group. TechnicalReport of Lenovo DeepComp 7000. Beijing, 2008
	Zhu M. LenovoDeepComp 6800 in China grid computing. In: Proceedings of Workshop of China HPC & Grid Computing, SuperComputing Conference. 2004
	Culler D, Gupta A, Sign J P. Parallel Computer Architecture: A Hardware/Software Approach. San Francisco: Morgan Kaufmann Publishers, 1998
	Buyya R. HighPerformance Cluster Computing: Architectures and Systems, Vol. 1. Upper Saddle River: Prentice Hall, 1999
	www.darpa.mil/ipto/Programs/hpcs/index.htm, USA, 2002
	Krishnamurthy A. HPCSbenchmarks and test and specification environments. In: Proceedings of SC05. 2005
	Xiao L, Zhu M. Exploiting the Capabilitiesof the Interconnection Network on Dawning-1000. Journal of Computer Science and Technology, 1999, 14(1): 49―55 doi: 10.1007/BF02952487
	Xiao L, Zhu M. Active Messages on Dawning1000. Nat. Res. Center for Intelligent Comput. Syst., Acad. Sinica, Beijing, 1996

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