CXL Topology-Aware and Expander-Driven Prefetching: Unlocking SSD Performance
Dongsuk Oh, Miryeong Kwon, Jiseon Kim, Eunjee Na, Junseok Moon, Hyunkyu Choi, Seonghyeon Jang, Hanjin Choi, Hongjoo Jung, Sangwon Lee, Myoungsoo Jung

TL;DR
This paper introduces ExPAND, a novel CXL-SSD prefetcher that improves performance by offloading cache prefetching, using a heterogeneous prediction algorithm, and providing accurate latency estimations, significantly boosting graph and CPU workloads.
Contribution
ExPAND is the first CXL-SSD prefetcher that leverages topology awareness and expander-driven algorithms to enhance prefetching accuracy and performance.
Findings
ExPAND improves graph application performance by 9.0×.
ExPAND enhances SPEC CPU performance by 14.7×.
It surpasses existing CXL-SSD prefetching strategies.
Abstract
Integrating compute express link (CXL) with SSDs allows scalable access to large memory but has slower speeds than DRAMs. We present ExPAND, an expander-driven CXL prefetcher that offloads last-level cache (LLC) prefetching from host CPU to CXL-SSDs. ExPAND uses a heterogeneous prediction algorithm for prefetching and ensures data consistency with CXL.mem's back-invalidation. We examine prefetch timeliness for accurate latency estimation. ExPAND, being aware of CXL multi-tiered switching, provides end-to-end latency for each CXL-SSD and precise prefetch timeliness estimations. Our method reduces CXL-SSD reliance and enables direct host cache access for most data. ExPAND enhances graph application performance and SPEC CPU's performance by 9.0 and 14.7, respectively, surpassing CXL-SSD pools with diverse prefetching strategies.
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