This article aims at making iterative optimization practical and usable by speeding up the evaluation of a large range of
optimizations. Instead of using a full run to evaluate a single program optimization, we take advantage of periods of stable
performance, called phases. For that purpose, we propose a low-overhead phase detection scheme geared toward fast optimization
space pruning, using code instrumentation and versioning implemented in a production compiler.
Our approach is driven by simplicity and practicality. We show that a simple phase detection scheme can be sufficient for
optimization space pruning. We also show it is possible to search for complex optimizations at run-time without resorting
to sophisticated dynamic compilation frameworks. Beyond iterative optimization, our approach also enables one to quickly design
self-tuned applications.
Considering 5 representative SpecFP2000 benchmarks, our approach speeds up iterative search for the best program optimizations
by a factor of 32 to 962. Phase prediction is 99.4% accurate on average, with an overhead of only 2.6%. The resulting self-tuned
implementations bring an average speed-up of 1.4.