Convergence of High-Performance & Machine Learning Workflows by Amal Gueroudji

The convergence of High-Performance Computing (HPC) and Machine Learning (ML) workflows offers transformative potential across scientific research, industrial applications, and emerging technologies. However, realizing seamless integration requires overcoming several critical challenges. Coupling distinct programming models—such as MPI for distributed HPC systems and Python-based frameworks prevalent in ML—introduces significant complexity, necessitating innovative interoperability mechanisms to harmonize these paradigms effectively. Performance characterization in this hybrid ecosystem demands advanced metrics and methodologies capable of capturing the nuances of diverse computational patterns, from dense numerical simulations typical of HPC to the sparse tensor operations of ML, where even established tools fall short. Additionally, efficient data streaming in composable environments presents a persistent bottleneck, as these workflows often rely on real-time data ingestion, transformation, and transfer across heterogeneous architectures. This presentation explores these challenges and highlights strategies for addressing them, emphasizing the importance of scalable, holistic solutions to achieve composability and efficiency in HPC-ML workflows.