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Section: New Results
Design of Experiments
A large amount of resources is spent writing, porting, and
optimizing scientific and industrial High Performance Computing
applications, which makes autotuning techniques fundamental to lower
the cost of leveraging the improvements on execution time and power
consumption provided by the latest software and hardware
platforms. Despite the need for economy, most autotuning techniques
still require a large budget of costly experimental measurements to
provide good results, while rarely providing exploitable knowledge
after optimization. In [40], we present a
user-transparent (white-box) autotuning technique based on Design of
Experiments that operates under tight budget constraints by
significantly reducing the measurements needed to find good
optimizations. Our approach enables users to make informed decisions
on which optimizations to pursue and when to stop. We present an
experimental evaluation of our approach and show it is capable of
leveraging user decisions to find the best global configuration of a
GPU Laplacian kernel using half of the measurement budget used by
other common autotuning techniques. We show that our approach is
also capable of finding speedups of up to