The DAQ needle in the big-data haystack

In the last three decades, HEP experiments have faced the challenge of manipulating larger and larger masses of data from increasingly complex, heterogeneous detectors with millions and then tens of millions of electronic channels. LHC experiments abandoned the monolithic architectures of the nineties in favor of a distributed approach, leveraging the appearence of high speed switched networks developed for digital telecommunication and the internet, and the corresponding increase of memory bandwidth available in off-the-shelf consumer equipment. This led to a generation of experiments where custom electronics triggers, analysing coarser-granularity “fast” data, are confined to the first phase of selection, where predictable latency and real time processing for a modest initial rate reduction are “a necessary evil”. Ever more sophisticated algorithms are projected for use in HL- LHC upgrades, using tracker data in the low-level selection in high multiplicity environments, and requiring extremely complex data interconnects. These systems are quickly obsolete and inflexible but must nonetheless survive and be maintained across the extremely long life span of current detectors.New high-bandwidth bidirectional links could make high-speed low-power full readout at the crossing rate a possibility already in the next decade. At the same time, massively parallel and distributed analysis of unstructured data produced by loosely connected, “intelligent” sources has become ubiquitous in commercial applications, while the mass of persistent data produced by e.g. the LHC experiments has made multiple pass, systematic, end-to-end offline processing increasingly burdensome.A possible evolution of DAQ and trigger architectures could lead to detectors with extremely deep asynchronous or even virtual pipelines, where data streams from the various detector channels are analysed and indexed in situ quasi-real-time using intelligent, pattern-driven data organization, and the final selection is operated as a distributed “search for interesting event parts”. A holistic approach is required to study the potential impact of these different developments on the design of detector readout, trigger and data acquisition systems in the next decades.