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CXL-Bench: Benchmarking Shared CXL Memory Access

  • Marcel Weisgut
    ,
  • Daniel Ritter
    ,
  • Florian Schmeller
    ,
  • ,
  • Tilmann Rabl
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Open access

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Original language

English

Publication milestones

  • Published - 2025

Publication status

Published - 2025

Host publication title

International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures

Abstract

Memory access paths between a CPU core and memory are increasingly complex. Data can be placed on local- or remote-socket memory, and on local- and remote-die memory on modern multi-die CPUs, affecting memory access performance. Cache-coherent inter-device interconnects, such as Compute Express Link (CXL), allow a CPU core to perform load and store instructions to memory of a peripheral device. Such accesses incur higher access latency than accesses to local-socket memory and increase the access path complexity. For database system developers, it is important to understand the performance implications of these complex memory architectures. In this work, we present CXL-Bench, a benchmark framework for quantifying access performance for different memory access paths. CXL-Bench provides many configuration options, such as memory access patterns, the operating system’s memory abstraction, cache bypass options, and a distributed mode for setups with multiple servers accessing memory of the same device. We demonstrate the utility of CXL-Bench by quantifying memory access characteristics of two servers accessing a shared CXL 1.1 memory device. Our results show that memory accesses of one server to the device affect the access performance of another server accessing the same device. On the other hand, memory (de)allocations using CXL memory configured as a character device complete quickly, making frequent re-allocation of CXL memory feasible.

Funding Details

We thank Seagate Technology LLC for their support and the anonymous reviewers for their feedback. This work was partially funded by SAP, the German Research Foundation (ref. 414984028), the European Union’s Horizon 2020 research and innovation programme(ref. 957407), and the Independent Research Fund Denmark’s Inge Lehmann program (grant agreement number 0171-00062B).

Related Event

Title

Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures

Event type

Workshop

Degree of recognition

International event

Date

01/09/2025 - 01/12/2025

Location

LondonUnited Kingdom