AI workloads present new challenges for traditional HPC architectures, particularly as compute demands outpace I/O performance, power constraints tighten, and budgets remain stretched. The need for high-throughput, low-latency data access at extreme scale is forcing a re-evaluation of storage architectures to maximize efficiency without incurring unsustainable costs.
This session will explore how standard Linux and open storage technologies enable AI and HPC workloads to achieve parallel file system performance on commodity hardware—without requiring specialized infrastructure. Topics include: • Scaling AI Storage with Standard Linux: Leveraging NFSv4.2 advancements, including pNFS and FlexFiles, to enable parallel I/O at extreme scales. • Next-Generation SSD Integration: How embedding parallel file system capabilities into SSDs reduces data movement overhead while maximizing power efficiency. • Accelerating AI with Localized Storage on GPU/CPU Servers: Techniques to optimize checkpointing and ephemeral data storage, reducing I/O bottlenecks and overall infrastructure costs.
The session will feature real-world examples of how these innovations are being deployed today to drive high-performance AI workloads while addressing power and cost constraints.
