REX-IO '24

High Performance Computing (HPC) applications are evolving to include not only traditional scale-up modeling and simulation bulk-synchronous workloads but also scale-out workloads like artificial intelligence (AI), data analytics methods, deep learning, big data and complex multi-step workflows. The emerging workloads introduce new I/O subsystems by advancing beyond the traditional two-tiered file system with potential performance bottlenecks in terms of I/O performance and scalability. Another performance aspect is the intensifying complexity of parallel file and storage systems in large-scale cluster environments. The changing landscape of emerging HPC workloads along with the ever increasing gap between the compute and storage performance capabilities reinforces the need for an in-depth understanding of extreme-scale I/O workloads and for rethinking existing data storage and management techniques. 

This workshop solicits novel work that characterizes I/O behavior and identifies the challenges in scientific data and storage management for emerging HPC workloads, introduces potential solutions to alleviate some of these challenges, and demonstrates the effectiveness of the proposed solutions. We envision that this workshop will contribute to the community by analyzing emerging HPC workloads, recognizing the gap in the data management methodologies, and providing novel techniques to improve I/O performance for the exascale supercomputing era and beyond.

Important Dates: (All Dates are Anywhere on Earth)

• Submissions open: TBA

• Submission deadline: TBA

• Notification to authors: TBA

• Camera-ready paper due: TBA

• Workshop date: September 24, 2024