Predicting Cross Architecture Performance of Source Codes using Graph Neural Networks
Date
2024-12
Authors
Pokhrel, Aashish
Major Professor
Mitra, Simanta
Advisor
Committee Member
Prabhu, Gurpur
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this creative component, we explore the application of Graph Neural Networks (GNNs) to predict the cross-architecture performance of source code, focusing specifically on intermediate representation-based features. The goal of this work is to identify patterns that can help optimize the performance of programs when executed on different architectures, such as CPUs and GPUs. By leveraging GNNs, we aim to capture the relationships and structures inherent in source code, which are often challenging for traditional performance prediction models. Our experiments show promising results in predicting performance variations and highlight potential opportunities for program parallelization based on GNN-derived insights.
Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
creative component
Comments
Rights Statement
CC0 1.0 Universal
Copyright
2024