7 citations · 18 across the 7 of their papers we have counts for
7 papers
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
Xingfu Wu, Prasanna Balaprakash, Michael Kruse +7
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…
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…
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…
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)
Xingfu Wu, Michael Kruse, Prasanna Balaprakash +4
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 met…
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…
Sparse Matrix Code Dependence Analysis Simplification at Compile Time
Mahdi Soltan Mohammadi, Kazem Cheshmi, Ganesh Gopalakrishnan +5
Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication…
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…