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GPU optimization techniques to accelerate optiGAN-a particle simulation GAN.

Anirudh Srikanth¹, Carlotta Trigila¹, Emilie Roncali^1,2

¹Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States of America.

Machine Learning: Science and Technology

|June 17, 2024

Summary

This summary is machine-generated.

Optimizing software for graphics processing units (GPUs) is crucial for training complex AI models. This study demonstrates GPU optimization techniques that achieved a 4.5x performance increase for training the optiGAN model.

Keywords:

Monte-Carlo simulation generative adversarial networks graphics processing unit multidimensional probability distributions performance optimization radiation detector

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Area of Science:

Artificial Intelligence
High-Performance Computing
Computational Physics

Background:

The complexity of AI models necessitates specialized hardware like Graphics Processing Units (GPUs) for efficient training.
Despite hardware advancements, a gap persists between computational demands and current GPU capacity.
Software optimization is essential to maximize hardware utilization and bridge this performance gap.

Purpose of the Study:

To present and analyze general GPU optimization techniques for efficient AI model training.
To demonstrate the application of these optimizations on the optiGAN model for generating optical photon distributions.
To evaluate the performance improvements achieved through these software optimizations.

Main Methods:

Implementation of general GPU optimization techniques during the training of the optiGAN model.
Utilizing an 8GB Nvidia Quadro RTX 4000 GPU for training and performance analysis.
Employing the Nvidia Nsight Systems profiler to measure execution time and memory consumption.

Main Results:

The applied GPU optimizations resulted in an approximate 4.5x increase in runtime performance compared to naive training.
Performance was evaluated based on execution time and memory usage, demonstrating significant efficiency gains.
Model performance was maintained without compromise despite the accelerated training.

Conclusions:

Software optimization techniques can substantially enhance GPU utilization for training complex AI models like optiGAN.
The developed optimization strategies offer a practical approach to accelerate the training of generative adversarial networks.
Future work will focus on scaling the optiGAN model across multiple GPUs for even greater computational efficiency.