PAISE 2024

Co-located with IPDPS 2024, 27th May 2024, San Francisco, USA.

From applications to hardware platforms, Edge computing is rapidly transforming the computing landscape. Based on the program and participation in the past years, and particularly drawing on the detailed feedback from in-person workshop attendees in 2023, PAISE 2024 will balance interaction-focused sessions and traditional technical talks. The objective is to enable organic discussions that augment traditional paper presentations, providing a critically needed opportunity to discuss the current trends and issues, to share visions and opinions, to collect feedback and to discuss solutions covering the following areas of edge computing:

• applications — computer vision, machine learning, analytics, IoT;

• data flows — processing pipeline of data from ingestion to archival, pipeline of AI from learning to inference;

• control flows — parallel and distributed programming models and runtimes for managing constrained resources, cybersecurity; and

• infrastructure — storage, compute, and connectivity conducive to resource-constrained and harsh edge environments.

Workshop Program

Keynote: Harnessing the Edge for Science

Abstract: Recent advances in edge devices are enabling data-driven, AI-enabled scientific workflows integrate distributed data sources. Combined with pervasively available computing resources, spanning HPC to the edge, these workflows can help us understand end-to-end phenomenon, drive experimentation, and facilitate important decision making. However, despite the growth of available digital data sources at the edge, and the ubiquity of non-trivial computational power for processing this data, realizing such science workflows remains challenging. This talk will explore a computing continuum spanning resources at the edges, in HPC centers and clouds, and in-between, and providing abstractions that can be harnessed to support science. The talk will also introduce recent research in programming abstractions that can express what data should be processed and when and where it should be processed, and autonomic middleware services that automate the discovery of resources and the orchestration of computations across these resources.”

Dr. Manish Parashar is Director of the Scientific Computing and Imaging Institute and Presidential Professor in the University of Utah’s Kahlert School of Computing. He recently completed an IPA term as Office Director of NSF’s Office of Advanced Cyberinfrastructure, where he oversaw investments in national cyberinfrastructure. He also served as co-chair of the National Science and Technology Council’s Subcommittee on the Future Advanced Computing Ecosystem and the National Artificial Intelligence Research Resource Task Force. Manish is a fellow of AAAS, ACM, and IEEE.

Manish’s research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering. Not only has he published extensively in these areas, but he has also deployed software systems that are in wide use today. Manish is the founding chair of the IEEE Technical Consortium on High Performance Computing (TCHPC) and serves on the editorial boards and organizing committees of a number of journals and international conferences and workshops.

He has received several awards for his research and leadership, including the 2023 Achievement Award in High Performance Distributed Computing and the 2023 Sidney Fernbach Memorial Award. He is a Fellow of AAAS, ACM, and IEEE.

Panelists: Future of AI@Edge

Tanwi Mallick, Argonne National Laboratory

Dr. Tanwi Mallick is an assistant computer scientist in the Mathematics and Computer Science Division at Argonne National Laboratory, where she previously held a postdoctoral appointment. Her research primarily focuses on spatiotemporal graph neural networks, uncertainty quantification, trustworthy scientific machine learning (SciML), foundation models, natural language processing, and high-performance computing. Tanwi also has experience working across various scientific domains, such as HPC network analysis, transportation systems, and climate science. Prior to her tenure at Argonne, she was a senior data scientist at General Electric. She obtained her Ph.D. in computer science from the Indian Institute of Technology, Kharagpur, India.

Shivakant Mishra, University of Colorado, Boulder

Dr. Shivakant Mishra is a professor in the Department of Computer Science at the University of Colorado, Boulder. He serves as the site co-director of the NSF IUCRC (Industry University Cooperative Research Center) on Pervasive Personalized Intelligence. Dr. Mishra is also a co-founder of The Colorado Research Center for Democracy and Technology and The CU CyberSafety Research Center at CU-Boulder. His current research focuses on developing system-level support for edge computing and digital twins at the edge, building socio-technical systems to empower environmental justice communities, strengthening democracy through technology, and investigating cybersafety issues in social networks. Throughout his career, he has conducted extensive research in areas such as mobile group recommendations, cyberbullying in social networks, misbehavior detection in online video chat systems, multiplayer online game analytics, secure and intrusion-tolerant sensor networks, delay-tolerant networking, and group communication systems.

Stefania Perri, University of Calabria

Dr. Stefania Perri is an Associate Professor of Electronics at the University of Calabria (UNICAL). Previously, she held the position of Adjunct Professor at the Department of Electrical and Computer Engineering at the University of Rochester, NY, USA. Her research focuses on the design of embedded on-chip systems and FPGAs, development of heterogeneous systems through hardware/software co-design methodologies, utilization of Quantum-Dot Cellular Automata (QCA) for designing binary circuits, algorithms tailored for specific hardware to enhance digital image processing capabilities, and the design of novel hardware architectures aimed at high-performance image processing systems. Dr. Perri received “best paper” awards in 2006, 2009, 2010, and 2016. She has also served as the Head of Research in several research projects funded by the MIUR and MISE Ministries, collaborating with electronics industry companies such as Beghelli and Technosystem Development.

Manish Parashar, University of Utah

Dr. Manish Parashar is Director of the Scientific Computing and Imaging (SCI) Institute, Chair in Computational Science and Engineering, and Presidential Professor, Kalhert School of Computing at the University of Utah. He very recently completed an IPA appointment at the National Science Foundation where he served as Office Director of the NSF Office of Advanced Cyberinfrastructure, as well as co-chair of the National Science and Technology Council’s Subcommittee on the Future Advanced Computing Ecosystem and the National Artificial Intelligence Research Resource Task Force (NAIRR). Manish’s research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering and has published extensively in these areas. He has received several awards for his research and leadership, including the 2023 Achievement Award in High Performance Distributed Computing and the 2023 Sidney Fernbach Memorial Award. He is a Fellow of AAAS, ACM, and IEEE.

Organizers

Steering Committee

• Pete Beckman, Mathematics and Computer Science Division, Argonne National Laboratory
• Ramachandran Vaidyanathan, Division of Electrical and Computer Engineering, Louisiana State University
• Eric Van Hensbergen, ARM Research

Program Committee

• Denis Trystram, Grenoble Alpes university
• Utku Gunay Acer, Nokia Bell Labs
• Nicolás Erdödy, Open Parallel Ltd
• Marco Brocanelli, The Ohio State University
• Daniel Balouek-Thomert, Inria
• Bernhard J. Berger, Hamburg University of Technology
• Rolf Drechsler, Universität Bremen
• Pekka Jääskeläinen, Intel & Tampere University
• Alexandre da Silva Veith, Nokia Bell Labs
• Marcos Assuncao, ETS Montreal
• Janwillem Swalens, Nokia Bell Labs
• Nirmit Desai, IBM
• Jens Domke, RIKEN Center for Computational Science (R-CCS)
• Hiroki Matsutani, Keio University
• Eric Van Hensbergen, ARM Research
• Michael Papka, Argonne National Laboratory and University of Illinois Chicago
• Kazutomo Yoshii, Argonne National Laboratory
• Issam Rais, UiT
• Aalap Tripathy, Hewlett Packard Enterprise
• Mohamed Wahib, RIKEN Center for Computational Science (R-CCS)

Publicity Chairs

• Marcin Copik, ETH Zürich
• Christopher A. Metz, Universität Bremen

General Chairs

• Laurent Lefèvre, Inria, Lyon

Program Chairs

• Istemi Ekin Akkus, Nokia Bell Labs
• Rajesh Sankaran, Mathematics and Computer Science Division, Argonne National Laboratory