Session

DescriptionThe exponential growth of data handled by HPC systems is driven by growing simulation fidelity and an explosion of affordable, high-resolution sensing devices. The rise of machine learning applications and other large-scale data analysis tasks imposes workload patterns on existing storage solutions that often lead to large, avoidable data copies and transfers, as well as contention near storage devices and in the network. This creates challenges for the reproducibility of science and thus limits trust in science. Computational storage is a promising solution to lower barriers to public data access and reduce the time and cost for many workloads that require search across or aggregation of large volumes of data. The session will feature four speakers from academia, government labs, and industry to give their perspectives on the challenges and opportunities of computational storage for scientific computing and how the technology may help to increase trust in science despite the data deluge.