Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning
Thursday, July 1, 2021 3:50 PM to 4:10 PM · 20 min. (Africa/Abidjan)
Stream#4
Information
Contributors:
Abstract:
Portable parallel programming models provide the potential for high performance and productivity, however they come with a multitude of runtime parameters that can have significant impact on execution performance. Selecting the optimal set of those parameters is non-trivial, so that HPC applications perform well in different system environments and on different input data sets, without the need of time consuming parameter exploration or major algorithmic adjustments. We present Artemis, a method for online, feedback-driven, automatic parameter tuning using machine learning that is generalizable and suitable for integration into high-performance codes. Artemis monitors execution at runtime and creates adaptive models for tuning execution parameters, while being minimally invasive in application development and runtime overhead. We demonstrate the effectiveness of Artemis by optimizing the execution times of three HPC proxy applications: Cleverleaf, LULESH, and Kokkos Kernels SpMV. Evaluation shows that Artemis selects the optimal execution policy with over 85% accuracy, has modest monitoring overhead of less than 9%, and increases execution speed by up to 47% despite its runtime overhead.
- David Beckingsale (Lawrence Livermore National Laboratory)
- Todd Gamblin (Lawrence Livermore National Laboratory)
- Alfredo Gimenez (Lawrence Livermore National Laboratory)
- Giorgis Giorgakoudis (Lawrence Livermore National Laboratory)
- Kevin Huck (University of Oregon)
- Allen Malony (University of Oregon)
- David Poliakoff (Sandia National Laboratories)
- Chad Wood (University of Oregon)
Abstract:
Portable parallel programming models provide the potential for high performance and productivity, however they come with a multitude of runtime parameters that can have significant impact on execution performance. Selecting the optimal set of those parameters is non-trivial, so that HPC applications perform well in different system environments and on different input data sets, without the need of time consuming parameter exploration or major algorithmic adjustments. We present Artemis, a method for online, feedback-driven, automatic parameter tuning using machine learning that is generalizable and suitable for integration into high-performance codes. Artemis monitors execution at runtime and creates adaptive models for tuning execution parameters, while being minimally invasive in application development and runtime overhead. We demonstrate the effectiveness of Artemis by optimizing the execution times of three HPC proxy applications: Cleverleaf, LULESH, and Kokkos Kernels SpMV. Evaluation shows that Artemis selects the optimal execution policy with over 85% accuracy, has modest monitoring overhead of less than 9%, and increases execution speed by up to 47% despite its runtime overhead.
