Register

 

Ray is a unified framework for scaling AI and general Python workflows. Outside of machine learning (ML), its core distributed runtime and data libraries can be used for writing parallel applications that launch multiple processes, both on the same node and across multiple cluster nodes. These processes can subsequently execute a variety of workloads, e.g. Numba-compiled functions, NumPy calculations, and even GPU-enabled codes.

In this webinar, we will focus on scaling Ray workflows to multiple HPC cluster nodes to speed up various (non-ML) numerical workflows. We will look at both a loosely coupled (embarrassingly parallel) problem with a slowly converging series (the harmonic series with some terms taken out) and a tightly coupled parallel problem involving an iterative Schwarz linear solver.