# Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation

## TL;DR

This paper explores the integration of machine learning with high-performance computing to enhance performance, introducing the Learning Everywhere paradigm and discussing its potential benefits, challenges, and applications across various domains.

## Contribution

It introduces the Learning Everywhere paradigm, emphasizing the combination of ML and traditional HPC for improved performance and discusses associated challenges and opportunities.

## Key findings

- Effective performance can be achieved by integrating learning with simulation-based approaches.
- The paradigm offers new opportunities across multiple domains.
- It raises open questions in computer science and cyberinfrastructure.

## Abstract

The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction between traditional HPC and ML approaches and motivates the Learning Everywhere paradigm for HPC. We introduce the concept of effective performance that one can achieve by combining learning methodologies with simulation-based approaches, and distinguish between traditional performance as measured by benchmark scores. To support the promise of integrating HPC and learning methods, this paper examines specific examples and opportunities across a series of domains. It concludes with a series of open computer science and cyberinfrastructure questions and challenges that the Learning Everywhere paradigm presents.