Panel on "AI for HPC"
DescriptionArtificial Intelligence (AI) enhances the speed, precision, and effectiveness of many applications and simulations of different fields, including scientific applications and large-scale HPC simulations and models. Recently, researchers have attempted to solve problems related to High-Performance Computing and Cyberinfrastructure, such as Scheduling and Resource Management, Device Mapping and Autotuning, Code Optimization and Compilers, Code Generation and Translation, etc., using AI and specifically Deep Learning. However, a major challenge of this type of research is that Deep Learning methods usually need large datasets, and unlike in other fields, comparatively fewer datasets are available for these tasks. Another major challenge of data-driven HPC research is the representation of the data or code. For example, some primary research questions on data-driven Code and Compiler Optimization remain unanswered: “Can there be a UNIVERSAL REPRESENTATION for code that will perform well for all tasks, or do we need to have different representations for multiple optimizations? Can DL models learn ENOUGH without any dynamic or profiling information? Can DL models learn from all the IMBALANCED and mostly UNLABELED data?”. This panel aims to identify and discuss the challenges and opportunities for applying Deep Learning to HPC. It presents a stimulating environment where the community can discuss topics relevant to HPC and AI. The panel intends to initiate research collaborations and provides an opportunity to receive feedback and opinions from domain experts and discover new ideas, directions, and potential solutions in data-driven HPC research.
Event Type
TimeMonday, 14 November 20223:30pm - 4:55pm CST
Registration Categories
AI-HPC Convergence
Extreme Scale Computing
Parallel Programming Languages and Models
Runtime Systems
Session Formats
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