28256

Performance Optimization using Multimodal Modeling and Heterogeneous GNN

Akash Dutta, Jordi Alcaraz, Ali TehraniJamsaz, Eduardo Cesar, Anna Sikora, Ali Jannesari
Iowa State University, Ames, IA, USA
arXiv:2304.12568 [cs.DC], (27 Apr 2023)

@misc{dutta2023performance,

   title={Performance Optimization using Multimodal Modeling and Heterogeneous GNN},

   author={Akash Dutta and Jordi Alcaraz and Ali TehraniJamsaz and Eduardo Cesar and Anna Sikora and Ali Jannesari},

   year={2023},

   eprint={2304.12568},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

Download Download (PDF)   View View   Source Source   

381

views

Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose search strategies, which often might not identify the best configurations or their time to convergence is a significant barrier. There is, thus, a need for a general purpose and efficient tuning approach that can be easily scaled and adapted to various tuning tasks. We propose a technique for tuning parallel code regions that is general enough to be adapted to multiple tasks. In this paper, we analyze IR-based programming models to make task-specific performance optimizations. To this end, we propose the Multimodal Graph Neural Network and Autoencoder (MGA) tuner, a multimodal deep learning based approach that adapts Heterogeneous Graph Neural Networks and Denoizing Autoencoders for modeling IR-based code representations that serve as separate modalities. This approach is used as part of our pipeline to model a syntax, semantics, and structure-aware IR-based code representation for tuning parallel code regions/kernels. We extensively experiment on OpenMP and OpenCL code regions/kernels obtained from PolyBench, Rodinia, STREAM, DataRaceBench, AMD SDK, NPB, NVIDIA SDK, Parboil, SHOC, and LULESH benchmarks. We apply our multimodal learning techniques to the tasks of i) optimizing the number of threads, scheduling policy and chunk size in OpenMP loops and, ii) identifying the best device for heterogeneous device mapping of OpenCL kernels. Our experiments show that this multimodal learning based approach outperforms the state-of-the-art in all experiments.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: