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The rapid evolution of artificial intelligence (AI) technologies is precipitating a profound transformation in data storage requirements, highlighting a potential bottleneck in AI advancement due to insufficient memory and storage capacities. This presentation examines the interplay between AI development and data storage technologies, focusing on the growing disparity between their respective growth rates.

In order to develop open source CXL ecosystem software it has proven useful to emulate CXL features within the QEMU project. In this talk, I will introduce the current major CXL features that QEMU can emulate and walk you through how to set up a Linux + QEMU CXL environment that will enable testing and developing new CXL features. In addition, I will highlight some of the limitations of QEMU CXL emulation.

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SDXI is a standard for a memory-to-memory data mover and acceleration interface that is extensible, forward-compatible, and independent of I/O interconnect technology. Among other features, SDXI standardizes an interface and architecture that can be abstracted or virtualized with a well-defined capability to quiesce, suspend, and resume the architectural state of a per-address-space data mover.

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.

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Main memory dominates data center server cost, and hence, data center operators are exploring alternative technologies such as CXL-attached memory to improve cost without jeopardizing performance. Introducing multiple tiers of memory introduces new challenges, such as selecting the appropriate memory configuration for a given workload mix. In particular, we observe that inefficient configurations increase cost by up to 2.6× for clients, and resource stranding increases cost by 2.2× for cloud operators.

DMTF Redfish® is now a very fully featured management ecosystem for server management, but what about storage? Isn’t that Swordfish? Well, actually …
While Swordfish provides the storage-centric functionality, it leverages all of its base functionality from Redfish. Storage users and implementers can take advantage of the latest features in Redfish directly, including:
• Fabric support for CXL, supporting Swordfish and Sunfish
• Enhanced audit logging
• Enhanced metrics instrumentation
• Enhanced modeling for thermal and cooling, SKU management

The SNIA/DMTF Swordfish storage management standard provides a rich data set for managing and maintaining awareness of storage infrastructure. In this talk I'll query data from a device conforming to the Swordfish standard API and use the data for common use cases in storage management, reporting, and asset management that we have in our bespoke systems management tools. I'll look at how the data is delivered and how the various data defined by the standard are used to build the relationships we require for those common use cases.