Postdoctoral Appointee. Argonne National Laboratory (ANL). US / Algeria.
“Convergence of High-Performance & Machine Learning Workflows“
18 February 2025
Abstract
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.
Bio
Amal Gueroudji is a Postdoctoral Appointment at Argonne National Laboratory, specializing in high-performance computing (HPC) and distributed computing. She earned her Ph.D. from Université Grenoble Alpes and conducted research at Maison de la Simulation, a collaborative laboratory involving CEA, CNRS, Université Paris-Saclay, and Université Versailles Saint-Quentin. Her work focused on integrating bulk synchronous parallel simulations with distributed task-based in situ data analysis, specifically coupling MPI codes with Dask distributed. This approach aimed to enhance the efficiency of data analytics workflows in HPC environments.
Amal holds engineering and master’s degrees in computer systems from the Higher National School of Computer Science (ESI, formerly INI) in Algeria. Her final year project involved automating CPU/GPU communications within the GPU backend of the Tiramisu Compiler, in collaboration with the Computer Science and Artificial Intelligence Laboratory at MIT.
At Argonne, she continues to advance research in workflows and data services for scientific computing, contributing to the Radix-io team. Her expertise includes high-performance computing, distributed computing, in situ analytics, task-based programming, and programming models.
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