Measuring Computer Performance by David J. Lilja

By David J. Lilja

Measuring laptop functionality units out the elemental suggestions utilized in reading and knowing the functionality of desktops. The emphasis is on useful tools of dimension, simulation, and analytical modeling. David Lilja discusses functionality metrics and offers designated insurance of the thoughts utilized in benchmark courses. as well as intuitive factors of key statistical instruments, he describes the final "design of experiments" strategy and indicates how the utmost quantity of data should be acquired with minimal attempt. gains comprise appendices directory universal likelihood distributions and statistical tables and a thesaurus of significant technical phrases.

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8. Chapter 4: Errors in Experimental Measurements ‘To free a man of error is to give, not to take away. 1 Accuracy, precision, and resolution In trying to measure and understand the performance of computer systems, we are constantly confronted by the nitty-gritty details of the real world. Unfortunately, these annoying details effectively introduce uncertainty into our measurements. We refer to these uncertainties in measurements as errors or noise. To determine how much uncertainty exists in our measurements, and, therefore, to determine what conclusions we can actually draw from them, we must use the tools and techniques of probability and statistics to quantify the errors.

From the performance analyst's point of view, these different measurements are all estimates of the true value of the duration of the event being measured. The differences in these measurements are unpredictable, however, and must be treated as ‘noise' or errors in the measurements. It is useful to classify all of the different sources of error into two different types, systematic errors, and random errors. Systematic errors are the result of some experimental ‘mistake,' such as some change in the experimental environment or an incorrect procedure, that introduces a bias into the measurements.

6) seconds. The intuition behind the formula This example highlights a potential source of confusion when trying to interpret what a confidence interval is actually telling us. 4). 7). That is, when we increase our confidence about the mean, we end up with a wider interval. However, this wider interval would then seem to imply that we now know less about the actual mean than when we used a lower confidence level. 6), which suggests that we now have even less precise knowledge about the actual mean.

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