14th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures
FLEXSCIENCE 2024
Pisa, Italy, June 3, 2024
Program
Date: June 3, 2024
Location: room Seminari Est, Department of Computer Science
14h00 - 16h00 Session 1 - Chair: Kento Sato
14h00 - Welcome (Alexandru Costan, Bogdan Nicolae, Kento Sato)
14h00 - 14h30 Proteus: Towards Intent-driven Automated Resource Management Framework for Edge Sensor Nodes, S. Ilager, D. Balouek, S. Kaddour, I. Brandic
14h30 - 15h00 Breaking the Memory Wall: A Study of I/O Patterns and GPU Memory Utilization for Hybrid CPU-GPU Offloaded Optimizers, A. Maurya, J. Ye, M. Rafique, F. Cappello, B. Nicolae
15h00 - 15h30 High-Throughput Computing: Case Study of Medical Image Processing Applications, M. Predescu, C. Samoila, E. Slusanschi, A. Gainaru
15h30 - 16h00 Hydra: Brokering Cloud and HPC Resources to Support the Execution of Heterogeneous Workloads at Scale, A. Alsaadi, M. Turilli, S. Jha
16h00 - 16h30 Coffee Break
16h30 - 18h30 Session 2 - Chair: Bogdan Nicolae
16h30 - 17h00 Towards Efficient Learning on the Computing Continuum: Advancing Dynamic Adaptation of Federated Learning, M. Valli, A. Costan, C. Tedeschi, L. Cudennec
17h00 - 18h00 Tutorial: A Control Theory Introduction for Computer Scientists, Q. Guilloteau
18h00 - 18h30 GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data, W. Jia, S. Jin, J. Wang, W. Niu, D. Tao, M. Yin
WORKSHop overview
Scientific computing applications generate enormous datasets that are continuously increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of modern big data analytics. Supported by the rise of artificial intelligence and deep learning, such enormous datasets are becoming valuable resources even beyond their original scope, opening new opportunities to learn patterns and extract new knowledge at large scale, potentially without human intervention. However, this leads to an increasing complexity of the workflows that combine traditional HPC simulations with big data analytics and AI applications. An initial wave that opened this direction was the shift from compute-intensive to data-intensive, which saw several ideas from big data analytics (in-situ processing, shipping computations close to data, complex and dynamic workflows) fused with the tightly coupled patterns addressed by the AI and the high performance computing ecosystems. In a quest to keep up with the complexity of the workflows, the design and operation of the infrastructures capable of running them efficiently at scale has evolved accordingly. Extreme heterogeneity at all levels (combinations of CPUs and accelerators, various types of memories and local storage and network links, parallel file systems and object stores, etc.) is now the norm. ideas pioneered by cloud and edge computing (aspects related to elasticity, multi-tenancy, geo-distributed processing, stream computing) are also beginning to be adopted in the HPC ecosystem (containerized workflows, on-demand jobs to complement batch jobs, streaming of experimental data from instruments directly to supercomputers, etc.). Thus, modern scientific applications need to be integrated into an entire Compute Continuum from the edge all the way to supercomputers and large data-centers using flexible infrastructures and middlewares.
The 14th workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures (FlexScience) will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running scientific computing workloads in such flexible ecosystems, across the Computing Continuum, focusing on emerging technologies and new convergence challenges that are not sufficiently addressed by the current generation of supercomputers and dedicated data centers. The workshop aims to address questions such as: what architectural changes to existing frameworks (hardware, operating systems, networking and/or programming models) are needed to support flexible computing? Dynamic information derived from remote instruments, coupled simulations, and sensor ensembles that stream data for real-time analysis and machine learning are important emerging trends. How can we leverage and adapt to these patterns? What scientific workloads are suitable candidates to take advantage of heterogeneity, elasticity and/or on-demand resources? What factors are limiting the adoption of a flexible design?
The workshop encourages interaction and cross-pollination between participants that are developing applications, algorithms, middleware and infrastructure and that are facing new challenges and opportunities to take advantage of flexible computing. The workshop will be an excellent place to help the community define the current state, determine future goals, and discuss promising technologies and techniques.
topics
Complex workflows at the intersection of HPC, Big Data and AI
Experimental evaluations of porting HPC/AI applications to clouds
Hybrid clouds that combine HPC data centers with public clouds in various scenarios (bursting, data sharing)
Interplay between Edge, Fog and Hybrid Clouds
Performance portability and related abstractions to hide the heterogeneity of resources
RRR (Robustness, Reconfigurability, Reproducibility) of complex workflows
Implementation and fine-tuning of high-performance AI and deep learning frameworks for clouds (e.g., Tensorflow, PyTorch, Horovod, etc.)
Scalability and cost-effective elasticity of AI and deep learning (e.g. data-parallel training) for cloud infrastructures
Virtualization, containers, and dynamic provisioning
Scalable and elastic cloud/HPC storage and I/O data management services and architectures
Data-intensive workloads and tools (e.g. caching) in clouds
Use of popular cloud building blocks (e.g., NoSQL databases) for scientific applications
Fault tolerance and reliability in cloud systems
Analysis of management complexity, cost, variability of cloud and IoT environments
SUBMISSION
Important Dates:
Paper submission deadline: April 3, 2024 AoE (March 15, 2024)
Paper notification: April 15, 2024
Camera ready papers: April 18, 2024
Workshop: June 3-4, 2024
Paper Categories:
Authors are invited to submit:
Full 8-page papers
Short/work-in-progress 5-page papers
Formatting:
Authors are invited to submit papers describing unpublished, original research. All submitted manuscripts should be formatted using the ACM Master Template with sigconf format (please be sure to use the current version). All necessary documentation can be found at: https://www.acm.org/publications/proceedings-template. Workshop papers should range from a minimum of 5 pages to a maximum of 8 pages. All papers must be in English. We use single-blind reviewing process, so please keep the authors names, publications, etc., in the text.
Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM Digital Library.
Paper submission:
Papers conforming to these guidelines should be submitted through HotCRP.
CHairs
Alexandru Costan, IRISA / INSA Rennes, France (alexandru.costan@irisa.fr)
Bogdan Nicolae, Argonne National Laboratory, USA (bogdan.nicolae@acm.org)
Kento Sato, RIKEN Center, Japan (kento.sato@riken.jp)
programme committee
Michael Sevilla, University of Santa Cruz, USA
Dongfang Zhao, University of Nevada, USA
Elena Apostol, Universitatea Politehnica Bucharest, Romania
Kevin Brown, Argonne National Laboratory, USA
Anthony Kougkas, Illinois Institute of Technology, USA
Ryan Chard, Argonne National Laboratory, USA
Teng Wang, Florida State University, USA
Takakki Fukai, RIKEN, Japan
Radu Prodan, University of Klagenfurt, Austria
Mustafa Rafique, Rochester Institute of Technology, USA
Michael Schoettner, University of Duesseldorf, Germany