We focus on a design-of-experiments methodology for developing empirical performance
models of GPU kernels. Recently, we developed an iterative active learning algorithm that
adaptively selects parameter configurations in batches for concurrent evaluation on CPU
architectures in order to build performance models over the parameter space. In this paper,
we illustrate the adoption of the algorithm when concurrent evaluations are not possible,
which is particularly useful in the absence of GPU clusters. We present an empirical study of …

Machine learning for performance modeling performance modeling may provide a much
more efficient methodology for coping with high-dimensional search spaces in auto tuning
algebraic performance models increasingly challenging statistical performance models: an
effective alternative small number of input-output points obtained from empirical evaluation
deployed to test and/or aid search, compiler, and auto tuning