Architecting GPU-Accelerated Supercomputing for Real-Time Clinical AI in Large Hospital Systems

Abstract

The current hospitals require quick and dependable computing platforms to facilitate real-time clinical artificial intelligence. The paper is quantitative research on designing GPU accelerated supercomputing system in large hospitals. The suggested platform is based on multi-GPU and multi-node architecture that implies serving medical imaging, patient monitoring, and clinical AI workloads. The experimental performance indicates that end-to-end latency that was 210ms on a single GPU dropped to 61ms on 8GPUs and throughput was increased to 238 samples per second compared to 48 samples per second. There was increased efficiency in the use of resources with the use of GPUs increasing to 88 percent. To a scale of 4 GPUs and 8 GPUs efficiency was above 0.89 and 0.78 respectively. The reliability tests had 99.8 system availability, mean recovery time of 18 seconds with no failed clinical activities recorded. These findings indicate that supercomputing architecture with a graphics card can address the next-generation clinical AI demands of real-time, scalable, and reliable computing needs of the large hospital setting.