By David J. Lilja
Measuring desktop functionality units out the basic ideas utilized in reading and realizing the functionality of computers. The emphasis is on sensible tools of size, simulation, and analytical modeling. David Lilja discusses functionality metrics and gives precise assurance of the options utilized in benchmark courses. as well as intuitive causes of key statistical instruments, he describes the overall "design of experiments" approach and exhibits how the utmost volume of data should be acquired with minimal attempt. good points contain appendices directory universal likelihood distributions and statistical tables and a word list of vital technical phrases.
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Extra info for Measuring Computer Performance
Recall that the above formula applies only when at least approximately 30 measurements have been made for each system, that is both n1 ≥ 30 and n2 ≥ 30. 5) for (c1, c2). The only change in this case is that the number of degrees of freedom to use in the t distribution, ndf is not simply n1 + n2 − 2, as might have been expected. Instead, it has been shown that the number of degrees of freedom in this case is approximately This value most likely will not be a whole number. Consequently, it should be rounded to the nearest whole number.
Chapter 5: Comparing Alternatives 45 46 Chapter 5: Comparing Alternatives In analyzing the performance of computer systems, however, we often want to know not only whether the differences between two configurations are statistically significant, but also the magnitude of that difference. As a result, instead of using only the hypothesis-testing technique, this chapter describes how to apply confidence intervals to compare two alternatives. This approach not only allows us to determine whether there is a statistically significant difference between the two alternatives, but also provides an indication of how large the difference is.
A. Measure the timer overhead time directly: for i = 1 to n1 start timer read timer stop timer end b. Measure the timer overhead time in a loop: for j = 1 to k start timer for i = 1 to n2 read timer end stop timer end For (a), generate a histogram of the times required to read the timer, and determine a 90% confidence interval for the mean time, for n1 = 10, 1,000, and 100,000. For (b), choose a reasonable value for n2 on the basis of your results from part (a). Then generate a histogram of the average times required to read the timer, and determine a 90% confidence interval for the mean time, for k = 10, 1,000, and 100,000.