Toward a CXL Memory Lake Architecture for Level-1 Data Scouting at CMS

Level-1 Data Scouting (L1DS) is a novel data acquisition subsystem at the Compact Muon Solenoid (CMS) Level-1 Trigger (L1T) that exposes the L1T event selection data primitives for online processing at the 40 MHz bunchcrossing rate of the Large Hadron Collider (LHC), enabling unbiased and unconventional analyses. An L1DS demonstrator has been operating since Run 3, relying on a ramdisk for ephemeral storage of incoming and intermediate data, accessible by the system’s units through a Network File System (NFS). With the High-Luminosity LHC (HL-LHC) and the CMS Phase 2 upgrade projected to enhance trigger resolutions, a high-performance shared memory system is key to retain real-time processing capabilities in Run 4. For this, we leverage the emerging Compute Express Link (CXL) open standard, which provides uniform and cache-coherent memory access from heterogeneous processing units, targeting a streamlined pipeline with minimized data movement over a memory lake shared among CPUs and GPUs. In this contribution, we present the integration of CXL-compliant shared memory into the L1DS demonstrator at CMS, including an overview of our approach’s design, benefits, and limitations. Furthermore, we evaluate CXL-based L1DS performance through analyses in heterogeneous contexts, supporting a discussion of the memory lake model and its use cases for the CMS community.

Authors

Institutions

Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701308
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Toward a CXL Memory Lake Architecture for Level-1 Data Scouting at CMS

Sabrina Giorgetti, Giovanna Lazzari Miotto, Christian Deldicque, R. Ardino et al.
EPJ Web of Conferences
Distributed and Parallel Computing Systems
article

Toward a CXL Memory Lake Architecture for Level-1 Data Scouting at CMS

Sabrina Giorgetti, Giovanna Lazzari Miotto, Christian Deldicque, R. Ardino, P. Zejdl, Hannes Sakulin, Thomas Owen James, Dinyar Rabady, Matteo Migliorini, Giovanni Petrucciani, Emilio Meschi, D. Gigi, A. Rácz, M. Dobson, Guilherme Paulino, Davide Cristoforetti
article en

Abstract

Level-1 Data Scouting (L1DS) is a novel data acquisition subsystem at the Compact Muon Solenoid (CMS) Level-1 Trigger (L1T) that exposes the L1T event selection data primitives for online processing at the 40 MHz bunchcrossing rate of the Large Hadron Collider (LHC), enabling unbiased and unconventional analyses. An L1DS demonstrator has been operating since Run 3, relying on a ramdisk for ephemeral storage of incoming and intermediate data, accessible by the system’s units through a Network File System (NFS). With the High-Luminosity LHC (HL-LHC) and the CMS Phase 2 upgrade projected to enhance trigger resolutions, a high-performance shared memory system is key to retain real-time processing capabilities in Run 4. For this, we leverage the emerging Compute Express Link (CXL) open standard, which provides uniform and cache-coherent memory access from heterogeneous processing units, targeting a streamlined pipeline with minimized data movement over a memory lake shared among CPUs and GPUs. In this contribution, we present the integration of CXL-compliant shared memory into the L1DS demonstrator at CMS, including an overview of our approach’s design, benefits, and limitations. Furthermore, we evaluate CXL-based L1DS performance through analyses in heterogeneous contexts, supporting a discussion of the memory lake model and its use cases for the CMS community.

EPJ Web of ConferencesVol. 337
University of Padua (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT), University of Turin (IT), European Organization for Nuclear Research (CH)
Micron Technology, CERN
Openalex Percentile: Top 67%
Distributed and Parallel Computing Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.