Steering Customized AI Architectures for HPC Scientific Workloads
DescriptionWe explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems.
We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid systems. In particular, we investigate how Graphcore’s IPU can accelerate hybrid HPC applications, beyond the originally intended AI workloads.
We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid systems. In particular, we investigate how Graphcore’s IPU can accelerate hybrid HPC applications, beyond the originally intended AI workloads.
