Measuring empirical computational complexity

The standard language for describing the asymptotic behavior of algorithms is theoretical computational complexity. We propose a method for describing the asymptotic behavior of programs in practice by measuring their empirical computational complexity. Our method involves running a program on workloads spanning several orders of magnitude in size, measuring their performance, and fitting these observations to a model that predicts performance as a function of workload size. Comparing these models to the programmer's expectations or to theoretical asymptotic bounds can reveal performance bugs or confirm that a program's performance scales as expected. Grouping and ranking program locations based on these models focuses attention on scalability-critical code. We describe our tool, the Trend Profiler (trend-prof), for constructing models of empirical computational complexity that predict how many times each basic block in a program runs as a linear (y = a + bx) or a powerlaw (...

• Type: Conference
• Year: 2007
• Where: SIGSOFT
• Authors: Simon Goldsmith, Alex Aiken, Daniel Shawcross Wilkerson
• Keywords: Empirical Computational Complexity, SIGSOFT 2007, Software Engineering, Theoretical Computational Complexity, Worst-case Theoretical Complexity