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Lec8Perf

# Lec8Perf - CS 475 Performance Evaluation Wim Bohm Colorado...

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1 CS 475: Performance Evaluation Wim Bohm Colorado State University Fall 2012 Analyzing Program Performance n In empirical Computer Science, we plot functions describing the run time (or the memory use) of a program: ¨ This can be as a function of the input size . We have seen this in e.g. cs320 or cs420, where we studied polynomial and exponential ( monotonically growing ) complexity. ¨ In this class we also study program performance as a function of the number of processors. n In this case the functions are positive and, hopefully decreasing. n Also we plot speedup curves, which are usually asymptotic

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2 Analyzing/Plotting Data n When you run a program for a number of inputs (n) on a parallel machine with a number of processors (p), you end up with performance data sets. You want to characterize these in (a set of) functions: x: input size, y: performance or x: #processors, y: performance. n To study (parallel) program’s performance, we often use plotting tools n gnuplot, excel, matlab … (in these slides: excel) n Let’s look at increasing functions first. Example: 3 data sets f, g and h What kinds of functions are f, g and h? 0 20 40 60 80 100 120 140 160 180 1 2 3 4 5 f g h n f(n) g(n) h(n) 1 2 9 2 2 12 18 6 3 36 35 24 4 80 68 68 5 150 131 162 Hard / impossible to infer exponential? which base? polynomial? which order?
3 Why are functions hard to infer? n Two problems: ¨ Very small domain (here 1. .5) n Try to get a large data domain ¨ Interpreting super-linear functions from plots is hard n All polynomials and exponentials swoop up Larger domain 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 2 4 6 8 10 12 14 f g h n f(n) g(n) h(n) 1 2 9 2 2 12 18 6 3 36 35 24 4 80 68 68 5 150 131 162 7 400 520 624 10 1100 4106

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Lec8Perf - CS 475 Performance Evaluation Wim Bohm Colorado...

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