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Talking about changes to the macOS SMB Client from Apple since 2022. What have we added, improved or just changed.

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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.