Streamlining CXL Adoption for Hyperscale Efficiency
<|reference_start|>Streamlining CXL Adoption for Hyperscale Efficiency: In our exploration of Composable Memory systems utilizing CXL, we focus on overcoming adoption barriers at Hyperscale, underscored by economic models demonstrating Total Cost of Ownership (TCO). While CXL addresses the pressing memory capacity needs of emerging Hyperscale applications, the escalating demands from evolving use cases such as AI outpace the capabilities of current CXL solutions. Hyperscalers resort to software-based memory (de)compression technology, alleviating memory capacity, storage, and network constraints but incurring a notable "Tax" on Compute CPU cycles. As a pivotal guide to the CXL community, Hyperscalers have formulated the groundbreaking Open Compute Project (OCP) Hyperscale CXL Tiered Memory Expander specification. If implemented, this specification lowers TCO adoption barriers, enabling diverse CXL deployments at both Hyperscaler and Enterprise levels. We present a CXL integrated solution, aligning with the aforementioned specification, introducing an energy-efficient, scalable, hardware-accelerated, Lossless Compressed Memory CXL Tier. This solution, slated for mid-2024 production and open for integration with Memory Expander controller manufacturers, offers 2-3X CXL memory compression in nanoseconds, delivering a 20-25% reduction in TCO for end customers without requiring additional physical slots. In our discussion, we pinpoint areas for collaborative innovation within the CXL Community to expedite software/hardware advancements for CXL Tiered Memory Expansion. Furthermore, we delve into unresolved challenges in Pooled deployment and explore potential solutions, collectively aiming to make CXL adoption a "No Brainer" at Hyperscale.<|reference_end|>
@article{arelakis2024streamlining,
    title={Streamlining CXL Adoption for Hyperscale Efficiency},
    author={Angelos Arelakis, Nilesh Shah, Yiannis Nikolakopoulos, Dimitrios
  Palyvos-Giannas},
    journal={arXiv preprint arXiv:2404.03551},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03551},
    primaryClass={cs.ET}
}