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TSim4CXL: Trace-Driven Simulation Framework for CXL-Based High-Performance Computing Systems

delete2026-01-01
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PRE
AI
J
Jaewoo Son
Y
Yoon, Youngchul
H
Ha, Onhoi *
DOI:10.1007/978-3-031-99854-6_6delete
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Abstract

Abstract

En 中文
Compute Express Link (CXL) is recognized as a revolutionary technology in high-performance computing (HPC) system design, driven by the growing demand for efficient and scalable memory solutions tailored to memory-centric workloads. However, despite its potential, evaluating CXL performance in real-world scenarios is challenging due to the lack of CXL hardware and the high costs of building a large-scale distributed system. To address this, we propose TSim4CXL, a novel trace-driven simulation framework for CXL-based HPC systems that provides accurate timing simulations within a practical timeframe. TSim4CXL separates computing resources from the CXL memory system, generating traces and simulating the memory system using SystemC's discrete-event modeling. By modeling the CXL interconnect at the protocol level with various configuration parameters, TSim4CXL allows us to explore the design space of HPC architecture. The accuracy of our CXL simulation model is validated using CXL hardware provided by Samsung Electronics. First, we compare load latencies using a custom microbenchmark on CXL hardware with simulation results and adjust the CXL parameters in our simulator accordingly. Second, we assess communication latency by running LAMMPS applications, ensuring the simulation results align with real-world performance. In addition, we perform design space exploration with two memory-centric applications, up to 25 CPU nodes for LAMMPS and 4 GPU nodes for LLM training. Furthermore, we compare the performance of target applications by executing multiple DRAM simulators, demonstrating how the memory bandwidth affects simulated time. These experiments prove the viability of the proposed simulation framework.
Keywords:
Compute Express Link(CXL)
High-Performance Computing (HPC)
Trace-driven Simulation

Journal

E
EURO-PAR 2025: PARALLEL PROCESSING, PT I
IF:
0
Papers:
28
Citations:
0

Organization

S
seoul national university (snu)
Scholars:
7.2W
Papers: 6.6W
Citations: 86
Cited Papers

Cited Papers

Computational CXL-Memory Solution for Accelerating Memory-Intensive Applications
err2023-01-01
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PREAI
errJoonseop Sim; Soohong Ahn; Taeyoung Ahn; Seungyong Lee; Myunghyun Rhee; Jooyoung Kim; Kwangsik Shin; Donguk Moon; Euiseok Kim; Kyoung Park
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PREAI
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An LPDDR-based CXL-PNM Platform for TCO-efficient Inference of Transformer-based Large Language Models
err2024-03-02
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PREAI
errSang-Soo Park; KyungSoo Kim; Jinin So; Jin Jung; Jonggeon Lee; Kyoungwan Woo; Nayeon Kim; Younghyun Lee; Hyungyo Kim; Yongsuk Kwon; Jinhyun Kim; Jieun Lee; YeonGon Cho; Yongmin Tai; Jeonghyeon Cho; Hoyoung Song; Jung Ho Ahn; Nam Sung Kim
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PREAI
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Pond: CXL-Based Memory Pooling Systems for Cloud Platforms
err2023-01-30
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errOAAI
errHuaicheng Li; Daniel S. Berger; Lisa Hsu; Daniel Ernst; Pantea Zardoshti; Stanko Novakovic; Monish Shah; Samir Rajadnya; Scott Lee; Ishwar Agarwal; Mark D. Hill; Marcus Fontoura; Ricardo Bianchini
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PREAI
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