Speaker
Description
The ePIC collaboration is developing a highly integrated, multi-purpose detector for the upcoming Electron-Ion Collider (EIC). A co-design approach between the detector and the computing enables a seamless data flow from detector readout to physics analysis, using streaming readout and AI. This system is aimed at accelerating scientific discovery and improving measurement precision through enhanced control of systematic uncertainties.
The ePIC computing model is designed to operate across a globally distributed network of resources, spanning the host laboratories, domestic institutions, and international partners. To organize this infrastructure effectively, the ePIC computing model introduces the concept of “Echelons,” which is informed by, but distinct from, the Tier model used at the LHC.
A defining feature of the model is its dual-host architecture, in which raw data is simultaneously streamed to two geographically distinct sites, forming symmetric and complete replicas of the full dataset in near real time. This “butterfly model” ensures that either site is technically capable of performing full downstream processing independently or in coordination, depending on the needs of the collaboration.
The nature of streaming data presents distinct challenges that shape the design and implementation of workflow and workload management. The system must be capable of operating under time-critical constraints, consuming fine-grained, quasi-continuous data streams. It must also respond flexibly to changing data-taking conditions, shifting availability of computing resources, and the possibility of faults across the distributed system. The result is an adaptive and highly automated computing model that emphasizes robustness and efficiency in a distributed environment.
With the design phase now substantially complete, the effort is transitioning to implementation, supported by active testbeds and functioning prototypes. This presentation will describe the computing model’s conceptual foundations, its unique features, and the progress made in realizing it.