We present Artemis, a method for online, feedback-driven, automatic parameter tuning using machine learning that is generalizable and suitable for integration ...

Abstract. Portable parallel programming models provide the potential for high performance and productivity, however they come with a multi- tude of runtime ...

In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning ...

Apr 21, 2020 · Title: Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning · Lawrence Livermore National Laboratory · Sandia ...

Portable parallel programming models provide the potential for high performance and productivity, however they come with a multitude of runtime.

First-Author Publications. [2021] Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning Chad Wood, Giorgis Georgakoudis ...

Artemis: automatic runtime tuning of parallel execution parameters using machine learning. C Wood, G Georgakoudis, D Beckingsale, D Poliakoff, A Gimenez, K Huck ...

Apollo is a distributed tuning framework for parallel applications. You instrument your code with the Apollo API, tell Apollo what the tuning options are.

This paper presents ATune, an automatic performance tuning tool that uses machine learning techniques to determine the program-specific optimal settings for a ...

This paper introduces a novel approach based on machine learning techniques to estimate the values of MPI runtime parameters that tries to achieve optimal ...