AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models
Authors/Creators
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1.
Friedrich-Alexander-Universität Erlangen-Nürnberg
Description
Code and container definitions for the AI application benchmarking framework presented in "AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models" (https://arxiv.org/abs/2603.16164). The framework measures throughput and energy efficiency of representative computer vision workloads (ResNet-50, ViT-L/16, Stable Diffusion v2) and large language model workloads (LLaMA 3 8B pre-training with LitGPT and inference serving with SGLang) under systematic GPU power-cap sweeps. Includes Apptainer container definitions and configurations used to evaluate NVIDIA H100, NVIDIA H200, and AMD MI300X accelerators on a per-node basis.
An updated version with extended code and additional experiments will be maintained at https://github.com/RRZE-HPC/hpc-ai-perf-bench.
Files
hpc-ai-perf-bench-efficiency.zip
Files
(298.4 MB)
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