An interpolation-based approach to multi-parameter performance modeling for heterogeneous systems

To effectively optimize applications for emerging heterogeneous architectures, compilers and synthesis tools must perform the challenging task of estimating the performance of different implementations and optimizations for different numbers and types of computational resources. Many performance-prediction techniques exist, but those approaches are specific to particular resources or applications, and are often not capable of prediction for all combinations of inputs. In this paper, we introduce an approach to multi-parameter performance modeling based on sampling and interpolation. This approach can be used in conjunction with execution time data, simulated or observed, to quickly perform performance estimation for any function, on any resource, with any combination of inputs. By evaluating a Kriging-based interpolator on a variety of functions and computational resources, we determine bounds on the accuracy of this approach, and show that an interpolation-based approach utilizing Kriging can effectively model execution time for most applications. We also show that Kriging is a highly effective interpolation technique for execution time, and can be up to four orders of magnitude more accurate than nearest-neighbor interpolation or radial basis function interpolation.