Near to Far: An Evaluation of Disaggregated Memory for In-Memory Data Processing

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Contributors

Abstract

Efficient in-memory data processing relies on the availability of sufficient resources, be it CPU time or available main memory. Traditional approaches are coping with resource limitations by either adding more processors or RAM sticks to a single server (scale-up) or by adding multiple servers to a network cluster (scale-out). Further, the InfiniBand interconnect enables Remote Direct Memory Access (RDMA) and thus enhances the possibilities of resource sharing between distinct servers. Resource disaggregation means the (dynamic) sharing of available hardware, e. g., through the network. This paradigm is now further enhanced by the specification of Compute Express Link (CXL). In this paper, we systematically evaluate the implications of memory expansion as a form of resource disaggregation from the perspective of in-memory data processing through the local Ultrapath Interconnect (UPI), RDMA via InfiniBand, and PCIe attached memory via CXL. Our results show that CXL yields behavior that is comparable to UPI and outperforms the inherently asynchronous RDMA connection. Further, we found that handling UPI-attached memory as a type of disaggregated resource can yield additional performance benefits.

Details

Original languageEnglish
Title of host publicationDIMES 2023 - Proceedings of the 2023 1st Workshop on Disruptive Memory Systems, Part of
PublisherAssociation for Computing Machinery, Inc
Pages16-22
Number of pages7
ISBN (electronic)979-8-4007-0300-3
Publication statusPublished - 23 Oct 2023
Peer-reviewedYes

Workshop

Title1st Workshop on Disruptive Memory Systems
Abbreviated titleDIMES 2023
Conference number1
Descriptionco-located with the 29th ACM Symposium on Operating Systems Principles (SOSP 2023)
Duration23 October 2023
Website
LocationRhein-Mosel-Halle
CityKoblenz
CountryGermany

External IDs

ORCID /0000-0001-8107-2775/work/194824068

Keywords

Keywords

  • CXL, memory disaggregation, RDMA, UPI