Location
PEARC - TBD
Incorporating AI capabilities into traditional HPC workflows introduces many challenges for application developers, as it brings together two
very different computational paradigms. Python can serve as a common language to bridge these domains, but, as a general-purpose language,
the exact interfaces to scientific applications, workflow design motifs, and runtimes that enable performance and scalability are still being
explored and developed. This tutorial will help attendees develop skills for building AI-coupled HPC workflows using the DragonHPC project
along with GPU-accelerated tools like PyTorch and vLLM for end-to-end workflow orchestration. Attendees will first learn the fundamentals of
using DragonHPC for Python multiprocessing on GPUs across multiple nodes. Hands-on exercises will then introduce them to aspects of the
DragonHPC API for orchestrating both Python and traditional HPC (MPI-based) applications, leveraging GPU acceleration, coupled through
fast data exchange with DragonHPC’s in-memory distributed dictionary. From there, we will explore a number of execution and data-transfer
motifs common to hybrid AI-HPC workflows. Attendees will interact with lead developers from DragonHPC and learn about its applications in the
workflow community.