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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front Comput Sci    0, Vol. Issue () : 339-346    https://doi.org/10.1007/s11704-012-2071-5
RESEARCH ARTICLE
How much power is needed for a billion-thread high-throughput server?
Zhiwei XU()
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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Abstract

With the advent of Internet services, big data and cloud computing, high-throughput computing has generated much research interest, especially on high-throughput cloud servers. However, three basic questions are still not satisfactorily answered: (1) What are the basic metrics (what throughput and high-throughput of what)? (2) What are the main factors most beneficial to increasing throughput? (3) Are there any fundamental constraints and how high can the throughput go? This article addresses these issues by utilizing the fifty-year progress in Little’s law, to reveal three fundamental relations among the seven basic quantities of throughput (λ), number of active threads (L), waiting time (W), system power (P), thread energy (E), Watts per thread ω, threads per Joule θ. In addition to Little’s law L = λW, we obtain P = λE and λ = Lωθ, under reasonable assumptions. These equations help give a first order estimation of performance and power consumption targets for billion-thread cloud servers.

Keywords high-throughput computing      billion-thread servers      power consumption      waiting time and latency      performance formulation      Little’s law     
Corresponding Author(s): XU Zhiwei,Email:zxu@ict.ac.cn   
Issue Date: 01 August 2012
 Cite this article:   
Zhiwei XU. How much power is needed for a billion-thread high-throughput server?[J]. Front Comput Sci, 0, (): 339-346.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-012-2071-5
https://academic.hep.com.cn/fcs/EN/Y0/V/I/339
1 Barroso L, Hoelzle U. The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synthesis Lectures on Computer Architecture , 2009, 4(1): 1-108
doi: 10.2200/S00193ED1V01Y200905CAC006
2 Cloudstone. http://radlab.cs.berkeley.edu/wiki/projects/cloudstone
3 Little J. Little’s law as viewed on its 50th anniversary. Operational Research , 2011, 59(3): 536-549
doi: 10.1287/opre.1110.0940
4 Little J, Graves S. Little’s law. In: Chhajed D, Lowe T J, eds. Building Intuition: Insights from Basic Operations Management Models and Principles. New York: Springer Science and Business Media LLC, 2008
5 Garland M, Kirk D. Understanding throughput-oriented architectures. Communications of the ACM , 2010, 53(11): 58-66
doi: 10.1145/1839676.1839694
6 Hanlon C. A conversation with john hennessy and david patterson. ACM Queue , 2006-2007, 4(10): 14-22
doi: 10.1145/1189276.1189286
7 Brumelle S L. On the relation between customer and time averages in queues. Journal of Applied Probability , 1971, 8(3): 508-520
doi: 10.2307/3212174
8 Heyman D, Stidham S J. The relation between customer and time averages in queues. Operational Research , 1980, 28(4): 983-994
doi: 10.1287/opre.28.4.983
9 Glanz J. Google details, and defends, its use of electricity. The New York Times , 2011
10 High-throughput computing. http://research.cs.wisc.edu/condor/htc. html. see also http://en.wikipedia.org/wiki/condor_high-throughput_computing_system and http://en.wikipedia.org/wiki/high-throughput_computing
11 Many-task computing. http://en.wikipedia.org/wiki/Many-task_computing
12 Little J. A proof for the queuing formula: L = λW. Operational Research , 1961, 9(3): 383-387
doi: 10.1287/opre.9.3.383
13 El-Taha M, Stidham S. Sample-Path Analysis of Queueing Systems. Springer Netherlands , 1999
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