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What We Can Learn from Persistent Memory for CXL
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2023
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Gesellschaft für Informatik e.V.
Zusammenfassung
With Persistent Memory (PMem) entering the long-established memory hierarchy, various assumptions about the performance and granularity of memory access have been disrupted. To adapt existing applications and design new systems, research focused on how to efficiently move data between different types of memory, how to handle varying access latency, and how to trade off price for performance. Even though Optane is now discontinued, we expect that the insights gained from previous PMem research apply to future work on Compute Express Link (CXL) attached memory. In this paper, we discuss how limited hardware availability impacts the performance generalization of new designs, how existing CPU components are not adapted towards different access characteristics, and how multi-tier memory setups offer different price-performance trade-offs. To support future CXL research in each of these areas, we discuss how our insights apply to CXL and which problems researchers may encounter along the way.