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arXiv · 2606.19365

Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures

Jeeho Ryoo·Yongchan Jung·Muhammad Ali Khaliq·Weidong Zhang·Jiatong Han·Byeong Kil Lee
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Abstract

Diffusion models have become essential for high-fidelity 3D MRI synthesis, yet their deployment remains constrained by substantial GPU resource demands arising from hundreds of U-Net evaluations per sample and a highly heterogeneous kernel behavior. This paper performs a comprehensive performance analysis of the state-of-the-art medical diffusion model, Med-DDPM, across three generations of NVIDIA architectures to study kernel-level runtime breakdowns, instruction-mix characteristics, memory system utilization, warp-level activities, and profiler priority-score estimates. We show that training is overwhelmingly dominated by cuDNN convolution and implicit-GEMM kernels, with inefficiencies arising from memory-access patterns, tensor-layout conversions, and limited Tensor Core utilization. Guided by these insights, we evaluate two architecture-aware optimizations TF32 Tensor Core activation and a 3D channels-last layout and demonstrate that they reduce SM cycles by up to 100x, cut dynamic instructions by 100x, raise Tensor Core utilization from 1.45 to 9.98x, and increase IPC by 7% on A100, all without degrading synthesis quality.

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Jeeho Ryoo, Yongchan Jung, Muhammad Ali Khaliq, Weidong Zhang, Jiatong Han, Byeong Kil Lee. 2026-06-11. Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures. https://doi.org/10.1145/3777884.3797012

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