Abstract: Fibre Channel extends its renowned compatibility and reliability with a new speed, 128GFC. This talk will discuss the newly completed 128GFC specification as well as uses of Fibre Channel in storage disaggregation and machine learning. The speakers, Rupin Mohan and Craig Carlson, have decades of experience in the architecture and standards definition of Fibre Channel and storage systems.

The recent AI explosion is reshaping storage architectures in data centers, where GPU servers increasingly need to access vast amounts of data on network-attached disaggregated storage servers for more scalability and cost-effectiveness. However, conventional CPU-centric servers encounter critical performance and scalability challenges. First, the software mechanisms required to access remote storage over the network consume considerable CPU resources.

The SNIA TC AI Taskforce is working on a paper on AI workloads and the storage requirements for those workloads. This presentation will be an introduction to the key AI workloads with a description of how they use the data transports and storage systems. This is intended to be a foundational level presentation that will give participants a basic working knowledge of the subject.

CXL offers unprecedented opportunities to design and build much larger application and compute arrangements than were available even a few years ago. The ability to connect memory subsystems and other compute resources through a switched network provides a dizzying array of possibilities for custom tailoring an environment for a particular workload.

This keynote will provide an in-depth overview of Ultra Ethernet.

In this talk, the author will explore the use of accelerators for the training and inference of models, and look at the proliferation of methods and models in the AI world.

Ultra Ethernet (UEC) is the hot new technology that supports today’s AI and HPC workloads. For AI, it is well understood that the expensive accelerators (GPUs) must be constantly supplied with data. In many cases data continues to reside on traditional storage networks which are not designed for the performance and scalability required for today’s HPC and AI workloads. These networks are expected to migrate to UEC for improved bandwidth, tail latency, and scale.