CXL-based memory disaggregation technology opens up a new direction for big data solution frameworks​

CXL-based memory disaggregation technology opens up a new direction for big data solution frameworks​
Figure 1. a comparison of the architecture between CAMEL’s CXL solution and conventional RDMA-based memory disaggregation. Credit: KAIST

A team from the Computer Architecture and Memory Systems Laboratory (CAMEL) at KAIST presented a new compute express link (CXL) solution whose directly accessible, and high-performance memory disaggregation opens new directions for big data memory processing. Professor Myoungsoo Jung said the team's technology significantly improves performance compared to existing remote direct memory access (RDMA)-based memory disaggregation.

CXL is a peripheral component interconnect-express (PCIe)-based new dynamic multi-protocol made for efficiently utilizing and accelerators. Many enterprise data centers and vendors are paying attention to it as the next-generation multi-protocol for the era of big data.

Emerging big data applications such as , graph analytics, and in-memory databases require large memory capacities. However, scaling out the via a prior memory interface like double data rate (DDR) is limited by the number of the central processing units (CPUs) and memory controllers. Therefore, memory disaggregation, which allows connecting a host to another host's memory or memory nodes, has appeared.

RDMA is a way that a host can directly access another host's memory via InfiniBand, the commonly used network protocol in . Nowadays, most existing memory disaggregation technologies employ RDMA to get a large memory capacity. As a result, a host can share another host's memory by transferring the data between local and remote memory.

CXL-based memory disaggregation technology opens up a new direction for big data solution frameworks​
Figure 2. A performance comparison between CAMEL’s CXL solution and prior RDMA-based disaggregation. Credit: KAIST

Although RDMA-based memory disaggregation provides a large memory capacity to a host, two critical problems exist. First, scaling out the memory still needs an extra CPU to be added. Since passive memory such as dynamic random-access memory (DRAM), cannot operate by itself, it should be controlled by the CPU. Second, redundant data copies and software fabric interventions for RDMA-based memory disaggregation cause longer access latency. For example, remote memory access latency in RDMA-based memory disaggregation is multiple orders of magnitude longer than local memory access.

To address these issues, Professor Jung's team developed the CXL-based memory disaggregation framework, including CXL-enabled customized CPUs, CXL devices, CXL switches, and CXL-aware operating system modules. The team's CXL device is a pure passive and directly accessible memory node that contains multiple DRAM dual inline memory modules (DIMMs) and a CXL memory controller. Since the CXL memory controller supports the memory in the CXL , a host can utilize the memory node without processor or software intervention. The team's CXL switch enables scaling out a host's memory capacity by hierarchically connecting multiple CXL devices to the CXL switch allowing more than hundreds of devices. Atop the switches and devices, the team's CXL-enabled operating system removes redundant data copy and protocol conversion exhibited by conventional RDMA, which can significantly decrease access latency to the memory nodes.

In a test comparing loading 64B (cacheline) data from memory pooling devices, CXL-based memory disaggregation showed 8.2 times higher data load performance than RDMA-based memory disaggregation and even similar performance to local DRAM memory. In the team's evaluations for a big data benchmark such as a machine learning-based test, CXL-based memory disaggregation technology also showed a maximum of 3.7 times higher performance than prior RDMA-based memory disaggregation technologies.

"Escaping from the conventional RDMA-based memory disaggregation, our CXL-based memory disaggregation framework can provide high scalability and performance for diverse datacenters and cloud service infrastructures," said Professor Jung. He went on to stress, "Our CXL-based memory disaggregation research will bring about a new paradigm for memory solutions that will lead the era of big data."

Provided by KAIST
Citation: CXL-based memory disaggregation technology opens up a new direction for big data solution frameworks​ (2022, March 16) retrieved 26 February 2024 from
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Explore further

'Memory disaggregation' for large-scale computing made practical


Feedback to editors