Machine learning-based auto-tuning for enhanced performance portability of OpenCL applications

2016 ◽  
Vol 29 (8) ◽  
pp. e4029 ◽  
Author(s):  
Thomas L. Falch ◽  
Anne C. Elster
Keyword(s):  
Machine Learning ◽  
Performance Portability ◽  
Auto Tuning

scholarly journals Collective Mind: Towards Practical and Collaborative Auto-Tuning

2014 ◽  
Vol 22 (4) ◽  
pp. 309-329 ◽  
Author(s):  
Grigori Fursin ◽  
Renato Miceli ◽  
Anton Lokhmotov ◽  
Michael Gerndt ◽  
Marc Baboulin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Reproducible Research ◽  
Computer Engineering ◽  
Code Size ◽  
Collective Mind ◽  
Research Material ◽  
Software And Hardware ◽  
Auto Tuning ◽  
Set Up

Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material atc-mind.org/repoto set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community.

scholarly journals Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles

2020 ◽  
Vol 34 (3) ◽  
pp. 357-374 ◽  
Author(s):  
JCS Kadupitiya ◽  
Geoffrey C Fox ◽  
Vikram Jadhao
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Message Passing ◽  
Message Passing Interface ◽  
Md Simulations ◽  
Computing Methods ◽  
Coarse Grained ◽  
Computational Time ◽  
Optimization Parameters ◽  
Auto Tuning

Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network–based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid Open Multi-Processing / Message Passing Interface (OpenMP/MPI) parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil–water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.

scholarly journals Parallel software auto-tuning using statistical modeling and machine learning

2018 ◽  
pp. 046-053
Author(s):  
A.Yu. Doroshenko ◽  
◽  
P.A. Ivanenko ◽  
O.S. Novak ◽  
O.A. Yatsenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Modeling ◽  
Parallel Software ◽  
Auto Tuning

scholarly journals Machine learning techniques for state recognition and auto-tuning in quantum dots

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Sandesh S. Kalantre ◽  
Justyna P. Zwolak ◽  
Stephen Ragole ◽  
Xingyao Wu ◽  
Neil M. Zimmerman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Dots ◽  
Machine Learning Techniques ◽  
State Recognition ◽  
Learning Techniques ◽  
Auto Tuning
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