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

Understand

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.