Infrastructure for Artificial Intelligence, Quantum and High Performance Computing

William Gropp (University of Illinois at Urbana-Champaign), Sujata Banerjee (VMware Research) and Ian Foster (University of Chicago) High Performance Computing (HPC), Artificial Intelligence (AI)/Machine Learning (ML), and Quantum Computing (QC) and communications offer immense opportunities for innovation and impact on society. Researchers in these areas depend on access to computing infrastructure, but these resources are in short supply and are typically siloed in support of their research communities, making it more difficult to pursue convergent and interdisciplinary research. Such research increasingly depends on complex workflows that require different resources for each stage. This paper argues that a more-holistic approach to computing infrastructure, one that recognizes both the convergence of some capabilities and the complementary capabilities from new computing approaches, be it commercial cloud to Quantum Computing, is needed to support computer science research. The types of infrastructure needed to support HPC and AI/ML share many features; GPU systems originally developed for HPC have become essential for ML, and those systems have further been optimized for ML, with features now being applied to HPC simulations.