Next-generation imaging technologies are producing a wealth of rich data to advance Life Sciences research. Artificial intelligence and machine learning can help researchers quickly derive insights from such data. However, many organizations do not have the infrastructures to handle the combination of enormous imaging data volumes and demanding analysis compute requirements.
What’s needed is an infrastructure that supports mixed genomic analysis and image analysis workloads. This paper discusses the new compute and storage demands for Cryogenic Electron Microscopy (CryoEM) and Lattice Light Sheet Microscopy (LLS) image analysis pipelines and the need for an easy to manage data storage foundation that delivers high performance in a reliable, scalable, and adaptable way.
This whitepaper was produced in conjunction with HPCwire, and sponsored by Panasas.