Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures
arXiv:2606.19365v1 Announce Type: new 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
The increasing sophistication and computational demands of 3D generative AI models, particularly in critical applications like medical imaging, are pushing the limits of current hardware optimization.
Optimizing the performance of generative AI models on existing GPU architectures is crucial for their widespread deployment and economic viability across various industries, including healthcare.
This research provides detailed insights into kernel-level performance bottlenecks, which can inform future hardware and software co-design, potentially making high-fidelity 3D AI more accessible and efficient.
- · NVIDIA
- · GPU manufacturers
- · AI model developers
- · Healthcare AI providers
- · Developers neglecting performance optimization
- · Users with limited computing resources
Improved performance of 3D generative diffusion models on current and next-gen GPUs.
Reduced operational costs and increased accessibility for advanced AI applications requiring 3D synthesis.
Acceleration of the adoption and commercialization of complex 3D AI across sectors, driving demand for optimized hardware and specialized software tooling.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG