Abstract:Modern computing systems are increasingly more complex, with their multicore CPUs and GPUs accelerators changing yearly, if not more often. It thus has become very challenging to write programs that efficiently use the associated complex memory systems and take advantage of the available parallelism. Autotuning addresses this by optimizing parameterized code to the targeted hardware by searching for the optimal set of parameters. Empirical autotuning has therefore gained interest during the past decades. While… Show more
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