CTWatch
November 2006 B
High Productivity Computing Systems and the Path Towards Usable Petascale Computing
Tzu-Yi Chen, Pomona College
Meghan Gunn, University of San Diego
Beth Simon, UC San Diego
Laura Carrington, San Diego Supercomputer Center
Allan Snavely, San Diego Supercomputer Center

2

Still, in theory, the time needed to run an application should be a function of just machine and application characteristics, both plotted in Figure 2. Our goal is to determine whether there is a straightforward way to use those characteristics to rank supercomputers based on their performance on real applications. While existing methods for predicting parallel performance (e.g., 11 12 13) could be used to rank machines, they are designed primarily to help users optimize their code and not to predict performance on different machines. As a result, they are application-specific and typically complex to build.14

In this paper we describe a metric for evaluating the quality of a proposed ranking. We show that if the machines are ranked using only results from simple benchmarks, those that measure the bandwidth to main memory and the latency to L1 cache are significantly better predictors of relative performance than peak flops. We next show how application traces can be used to improve rankings, without resorting to detailed performance prediction.

Figure 2(a)Figure 2(b)

Figure 2. (a) A plot of machine characteristics for a subset of the systems in Table 5. (b) A plot of application characteristics for a subset of the applications in Table 4. For both, the highest count for each characteristic is normalized to one.

Pages: 1 2 3 4 5 6 7 8 9

Reference this article
"Metrics for Ranking the Performance of Supercomputers ," CTWatch Quarterly, Volume 2, Number 4B, November 2006 B. http://www.ctwatch.org/quarterly/articles/2006/11/metrics-for-ranking-the-performance-of-supercomputers/

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