Session

DescriptionDeep learning (DL) is increasingly employed in various scientific domains such as cosmology, medical analysis and diagnosis, geophysics, biology, etc. In addition, ready-to-use DL packages facilitate the usage of this powerful technique for the broader scientific community. However, the challenge arises as dataset size and model complexity increase. While, nowadays, traditional numerical methods commonly benefit from HPC environments, this is not yet the case for scalable DL frameworks. This minisymposium aims to share experiences in recent advances of DL applications and algorithms at scale and to provide a platform for knowledge exchange. The minisymposium gives an overview of distributed DL and parallelization strategies, addresses scalable communication-efficient algorithms for machine learning, and finally presents two distributed DL applications based on convolutional neural networks in cosmology research and in the pharmaceutical industry, running on a supercomputer and a cloud computing environment, respectively.