2 code implementations • 9 Jan 2024 • Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul Hovland, Mary Hall, Rong Ge, Prasanna Balaprakash
We introduce the first generative TL-based autotuning approach based on the Gaussian copula (GC) to model the high-performing regions of the search space from prior data and then generate high-performing configurations for new tasks.
1 code implementation • 28 Mar 2023 • Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary Hall
As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging.
no code implementations • 10 May 2021 • Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul Hovland, Mary Hall
The search space exposed by the transformation pragmas is a tree, wherein each node represents a specific combination of loop transformations that can be applied to the code resulting from the parent node's loop transformations.
1 code implementation • 27 Apr 2021 • Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul Hovland, Valerie Taylor, Mary Hall
In this paper, we develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness.
no code implementations • 15 Oct 2020 • Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul Hovland, Valerie Taylor, Mary Hall
An autotuning is an approach that explores a search space of possible implementations/configurations of a kernel or an application by selecting and evaluating a subset of implementations/configurations on a target platform and/or use models to identify a high performance implementation/configuration.
no code implementations • 13 Oct 2020 • Michael Kruse, Hal Finkel, Xingfu Wu
In this paper we propose a loop transformation search space that takes the form of a tree, in contrast to previous approaches that usually use vector spaces to represent loop optimization configurations.
1 code implementation • 6 Oct 2019 • Michael Kruse, Hal Finkel
Adding a pragma directive into the source code is arguably easier than rewriting it, for instance for loop unrolling.