Julia with MPI: Challenges and Best Practices
DescriptionIn this talk, we will delve into the challenges of using Julia with MPI and how to overcome them. Being a dynamic language, Julia presents unique issues that may not be familiar to users of static languages such as C or Fortran. We will explore common pitfalls that arise from configuring binary dependencies, compiling, and managing garbage collection, which can lead to errors and scaling bottlenecks. To tackle these challenges, we will suggest practical approaches and tools for identifying and fixing these issues.
TimeWednesday, June 2811:30 - 12:00 CEST
Computer Science, Machine Learning, and Applied Mathematics