Please check out our new Mochi Read the Docs page!  This is the official source for all Mochi documentation.  We will be expanding and updating material on that page over time.

Please stop by and check out the following events featuring Mochi technology at SC18!

• Qing Zheng et al. “Scaling Embedded In Situ Indexing with DeltaFS”, Technical program presentation.
• Matthieu Dorier et al. “Methodology for the Rapid Development of Scalable HPC Data Services”, PDSW workshop presentation.
• Jerome Soumagne et al. “Enabling Data Services for HPC”, Birds of a Feather session.

Please sign up for the mochi-devel mailing list if you would like to participate in technical discussions, ask questions, or keep up to date with changes in the Mochi software packages.

The modern DOE scientific computing portfolio consists of a rich ecosystem of simulation, data analytics, and learning applications, with many distinct data management and analysis needs. The objective of the Mochi project is to design methodologies and tools that allow for the rapid development of distributed data services in support of DOE science. An important aspect of Mochi is composition: common capabilities such as communication, data storage, concurrency management, and group membership are provided under Mochi along with building blocks such as BLOB and key-value stores. These building blocks are mixed together to provide specialized service implementations catering to specific platforms and science needs. Current Mochi research directions include unifying management of disparate data classes from scientific campaigns and applying learning and artificial intelligence to improve the adaptability of data services on heterogeneous DOE platforms.
The Mochi project is a collaboration between Argonne National Laboratory, Los Alamos National Laboratory, Carnegie Mellon University, and the HDF Group. However, Mochi is also bigger than just these partners: Mochi is an open ecosystem enabling the development of a variety of services both within the DOE and internationally.