  Computers are deterministic dynamical systems (CHAOS 19:033124, 2009). Among
other things, that implies that one should be able to use deterministic
forecast rules to predict their behavior. That statement is sometimes-but not
always-true. The memory and processor loads of some simple programs are easy to
predict, for example, but those of more-complex programs like compilers are
not. The goal of this paper is to determine why that is the case. We conjecture
that, in practice, complexity can effectively overwhelm the predictive power of
deterministic forecast models. To explore that, we build models of a number of
performance traces from different programs running on different Intel-based
computers. We then calculate the permutation entropy-a temporal entropy metric
that uses ordinal analysis-of those traces and correlate those values against
the prediction success
