Probabilistic prediction of program performance.

Abstract

The time it will take to run a program on a large problem size is estimated by sampling several smaller instances of the problem. Confidence intervals for the prediction are included. A time budget for sampling is used, so the estimate can be made quickly (but less precisely) or more slowly with greater accuracy. How to pick the sample points optimally under some simple assumptions is derived and proven. Overall accuracy is comparable to simulation or instruction counting, with much faster results; however, the results only apply to the given machine, not other platforms.