CXLfork: Fast Remote Fork over CXL Fabrics

The shared and distributed memory capabilities of the emerging Compute Express Link (CXL) interconnect urge us to rethink the traditional interfaces of system software. In this paper, we explore one such interface: remote fork using CXL-attached shared memory for cluster-wide process cloning. We present CXLfork, a remote fork interface that realizes close to zero-serialization, zero-copy process cloning across nodes over CXL fabrics. CXLfork utilizes globally-shared CXL memory for cluster-wide deduplication of process states. It also enables fine-grained control of state tiering between local and CXL memory. We use CXLfork to develop CXL-porter, an efficient horizontal autoscaler for serverless functions deployed on CXL fabrics. CXLfork minimizes cold-start overhead without sacrificing local memory. CXLfork attains restore latency close to that of a local fork, outperforming state-of-practice by 2.26x on average, and reducing local memory consumption by 87% on average.

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Publication Details

Published
2025-03-27
DOI
https://doi.org/10.1145/3676641.3715988
Citations
10
Primary Topic
Interconnection Networks and Systems
Type
article
Field-Weighted Citation Impact
14.87
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article

CXLfork: Fast Remote Fork over CXL Fabrics

Stratos Psomadakis, Tianyin Xu, Chloe Alverti, Shashwat Jaiswal et al.
10 citations
Interconnection Networks and Systems
14.87
article

CXLfork: Fast Remote Fork over CXL Fabrics

Stratos Psomadakis, Tianyin Xu, Chloe Alverti, Shashwat Jaiswal, Josep Torrellas, Burak Ocalan
article en
10 citations

Abstract

The shared and distributed memory capabilities of the emerging Compute Express Link (CXL) interconnect urge us to rethink the traditional interfaces of system software. In this paper, we explore one such interface: remote fork using CXL-attached shared memory for cluster-wide process cloning. We present CXLfork, a remote fork interface that realizes close to zero-serialization, zero-copy process cloning across nodes over CXL fabrics. CXLfork utilizes globally-shared CXL memory for cluster-wide deduplication of process states. It also enables fine-grained control of state tiering between local and CXL memory. We use CXLfork to develop CXL-porter, an efficient horizontal autoscaler for serverless functions deployed on CXL fabrics. CXLfork minimizes cold-start overhead without sacrificing local memory. CXLfork attains restore latency close to that of a local fork, outperforming state-of-practice by 2.26x on average, and reducing local memory consumption by 87% on average.

University of Illinois Urbana-Champaign (US), National Technical University of Athens (GR)
Openalex Percentile: Top 1%
Interconnection Networks and Systems
14.87
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