Mochi, developed by Argonne National Laboratory, is a game-changing open-source tool for rapid development of customized data services supporting high-performance computing (HPC), big data and large-scale learning across many scientific fields. The Mochi framework enables composition of distributed data services from a collection of connectable modules and subservices. These customized data services require relatively small amounts of new code, can generate high performance on HPC platforms, provide capable and productive interfaces and abstractions for various applications, and are readily adapted when new technologies are deployed.
Rather than forcing all applications to use a one-size-fits-all data staging and I/O software configuration, Mochi allows each application to use a data service specialized to its needs and access patterns. Already, Mochi is being incorporated to analyze data from particle accelerators, which has applications in fields such as medicine and materials science; study particle simulation data, with the goal of finding new sources of energy, such as nuclear fusion; help train machine learning models that can be used to identify cancer treatments; and build the data store for an upcoming exascale platform.