Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures

• URL: http://arxiv.org/abs/2504.11750v1
• Date: Wed, 16 Apr 2025 04:02:39 GMT
• Title: Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures
• Authors: Prabhu Vellaisamy, Thomas Labonte, Sourav Chakraborty, Matt Turner, Samantika Sury, John Paul Shen,
• Abstract summary: Large language model (LLM)-based inference workloads increasingly dominate data center costs and resource utilization.<n>This paper presents an in-depth analysis of inference behavior on loosely-coupled ( PCIe A100/H100) and closely-coupled (GH200) systems.
• Score: 3.2645124275315163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM)-based inference workloads increasingly dominate data center costs and resource utilization. Therefore, understanding the inference workload characteristics on evolving CPU-GPU coupled architectures is crucial for optimization. This paper presents an in-depth analysis of LLM inference behavior on loosely-coupled (PCIe A100/H100) and closely-coupled (GH200) systems. We analyze performance dynamics using fine-grained operator-to-kernel trace analysis, facilitated by our novel profiler SKIP and metrics like Total Kernel Launch and Queuing Time (TKLQT). Results show that closely-coupled (CC) GH200 significantly outperforms loosely-coupled (LC) systems at large batch sizes, achieving 1.9x-2.7x faster prefill latency for Llama 3.2-1B. However, our analysis also reveals that GH200 remains CPU-bound up to 4x larger batch sizes than LC systems. In this extended CPU-bound region, we identify the performance characteristics of the Grace CPU as a key factor contributing to higher inference latency at low batch sizes on GH200. We demonstrate that TKLQT accurately identifies this CPU/GPU-bound transition point. Based on this analysis, we further show that kernel fusion offers significant potential to mitigate GH200's low-batch latency bottleneck by reducing kernel launch overhead. This detailed kernel-level characterization provides critical insights for optimizing diverse CPU-GPU coupling strategies. This work is an initial effort, and we plan to explore other major AI/DL workloads that demand different degrees of CPU-GPU heterogeneous architectures.

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