A Client/Server Architecture for Supporting Science Data Using EPICS Version 4
The Phase 1 grant that serves as a precursor to this proposal, prototyped complex storage techniques for high speed structured data that is being produced in accelerator diagnostics and beam line experiments. It demonstrates the technologies that can be used to archive and retrieve complex data structures and provide the performance required by our new accelerators, instrumentations, and detectors. Phase 2 is proposed to develop a high-performance platform for data acquisition and analysis to provide physicists and operators a better understanding of the beam dynamics. This proposal includes developing a platform for reading 109 MHz data at 10 KHz rates through a multicore front end processor, archiving the data to an archive repository that is then indexed for fast retrieval. The data is then retrieved from this data archive, integrated with the scalar data, to provide data sets to client applications for analysis, use in feedback, and to aid in identifying problem with the instrumentation, plant, beam steering, or model. This development is built on EPICS version 4 , which is being successfully deployed to implement physics applications. Through prior SBIR grants, EPICS version 4 has a solid communication protocol for middle layer services (PVAccess), structured data representation and methods for efficient transportation and access (PVData), an operational hierarchical record environment (JAVA IOC), and prototypes for standard structured data (Normative Types). This work was further developed through project funding to successfully deploy the first service based physics application environment with demonstrated services that provide arbitrary object views, save sets, model, lattice, and unit conversion. Thin client physics applications have been developed in Python that implement quad centering, orbit display, bump control, and slow orbit feedback. This service based architecture has provided a very modular and robust environment that enables commissioning teams to rapidly develop and deploy small scripts that build on powerful services. These services are all built on relational database data stores and scalar data. The work proposed herein, builds on these previous successes to provide data acquisition of high speed data for online analysis clients.