Probabilistic auto-tuning for architectures with complex constraints
Benjamin Ylvisaker, Scott Hauck · 2011
It is hard to optimize applications for coprocessor accelerator architectures, like FPGAs and GPUs, because application parameters must be tuned carefully to the size of the target architecture. Moreover, some combinations of parameters simply do not work, because they lead to overuse of a constrained resource. Applying auto-tuning---the use of search algorithms and empirical feedback to optimize programs---is an attractive solution, but tuning in the presence of unpredictable failures is not addressed well by existing auto-tuning methods.