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The paper introduces CXL-ClusterSim, a simulation framework combining gem5 and SST, to model and evaluate CXL-based disaggregated memory systems. This framework addresses the lack of simulation tools for exploring the design space of disaggregated memory, which is crucial for efficiently utilizing DRAM in large-scale AI workloads. CXL-ClusterSim enables scalable and flexible simulations, allowing researchers to explore hardware/software co-design and performance optimization opportunities in CXL-based memory disaggregation.
Finally, a simulation framework lets you explore the performance benefits of CXL-based disaggregated memory, a key technology for efficient large-scale AI.
Large-scale AI training and inference require hundreds of gigabytes to terabytes of DRAM with high peak to average utilization ratios, resulting in overprovisioning. In cloud computing, DRAM constitutes a significant share of the cost. Yet, as shown by recent articles, DRAM is heavily under utilized. Memory disaggregation is a solution to both these problems. With the advent of the CXL protocol, there is renewed interest in designing and optimizing computing systems with disaggregated memory. However, at present, there are limited simulation tools available for exploring the design space and evaluating the performance tradeoffs in computer systems with disaggregated memory. In this paper, we propose CXL-ClusterSim, a full-system modeling and simulation framework by combining the gem5 simulator for fidelity, with the Structural Simulation Toolkit (SST) for parallel simulation. We outline the challenges in creating this simulation infrastructure and present a design that is scalable, flexible, and reasonably fast to help computer architects to explore the design space of CXL-based disaggregated memory and identify new opportunities for hardware/software codesign and performance optimization.