Thorsten Kurth works at NVIDIA on optimizing scientific codes for GPU based supercomputers. His main focus is on providing optimized deep learning applications for HPC systems. These include end-to-end optimizations such as input pipeline including IO tuning, distributed training and data visualization.
Before he joined NVIDIA, Thorsten worked at NERSC with the application readiness team to deliver optimized codes for the NERSC HPC infrastructure. He was leading the Learning application category of the NERSC Exascale Science Application Program (NESAP), targeting at improving experimental and observational data analysis or simulation codes using machine learning and artificial intelligence methods.
In 2018 he was awarded the Gordon Bell Prize for the first Deep Learning application which achieved more than 1 ExaOp peak performance on the OLCF Summit HPC system.
ACM Gordon Bell COVID Finalist
Machine Learning, Deep Learning and Artificial Intelligence