Publications (12)
Transfer-Learning-Based Autotuning Using Gaussian Copula
Thomas Randall, Jaehoon Koo, Brice Videau +6
As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increase…
Polyhedral Specification and Code Generation of Sparse Tensor Contraction with Co-Iteration
Tuowen Zhao, Tobi Popoola, Mary Hall +2
This paper presents a code generator for sparse tensor contraction computations. It leverages a mathematical representation of loop nest computations in the sparse polyhedral frame…
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
Xingfu Wu, Michael Kruse, Prasanna Balaprakash +4
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 implement…
Rethinking Abstractions for Big Data: Why, Where, How, and What
Mary Hall, Robert M. Kirby, Feifei Li +6
Big data refers to large and complex data sets that, under existing approaches, exceed the capacity and capability of current compute platforms, systems software, analytical tools…
Integrating ytopt and libEnsemble to Autotune OpenMC
Xingfu Wu, John R. Tramm, Jeffrey Larson +7
ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that…
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations
Jaehoon Koo, Prasanna Balaprakash, Michael Kruse +3
Polly is the LLVM project's polyhedral loop nest optimizer. Recently, user-directed loop transformation pragmas were proposed based on LLVM/Clang and Polly. The search space expose…