This paper investigates the applicability of iterative search techniques in program optimisation. Iterative compilation is usually considered too expensive for general purpose computing but is applicable to embedded applications where the cost is easily amortised over the number of embedded systems produced. This paper presents a case study, where an iterative search algorithm is used to investigate a nonlinear transformation space and find the fastest execution time within a fixed number of evaluations. By using execution time as feedback, it searches a large but restricted transformation space and shows performance improvement over existing approaches. We show that in the case of large transformation spaces, we can achieve within 0.3% of the best possible time by visiting less then 0.25% of the space using a simple algorithm and find the minimum after visiting less than 1% of the space
