Automatic multi-objective optimization of parameters for hardware and code optimizations

Recent computer architectures can be configured in lots of different ways. To explore this huge design space, system simulators are typically used. As performance is no longer the only decisive factor but also e.g. power usage or the re source usage of the system it became very hard for designers to select optimal configurations. In this article we use a multi-objective design space exploration tool called FADSE to explore the vast design space of the Grid Alu Processor (GAP) and its post-link optimizer called GAPtimize. We improved FADSE with techniques to make it more robust against failures and to speed up evaluations through parallel processing. For the GAP, we present an approximation of the hardware complexity as second objective besides execution time. Mining of functions applied as a whole program optimization with GAPtimize is used as example for a code optimization. We show that FADSE is able to thoroughly explore the design space for both GAP and GAPtimize and it can find an ap proximation of the Pareto frontier consisting of near-optimal individuals in moderate time.