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Two major trends can be identified in the development of trigger and DAQ systems
for particle-physics experiments: the massive use of general-purpose commodity sys-
tems such as commercial PC farms for data acquisition, and the reduction of trigger
levels implemented in hardware, towards a pure software selection system (trigger-
less).
The NA62 experiment at the CERN SPS aims at measuring an ultra-rare decay
of the charged kaon (K+-> pi nu nubar); the signal has to be extracted from a huge
background which is ten orders of magnitude more frequent. With an input particle
rate of 10 MHz, some tens of thousands detector channels and the requirement of
avoiding zero suppression as much as possible, triggerless readout into PCs is not
affordable.
The very innovative approach presented here aims at exploiting the parallel com-
puting power of commercial GPUs (Graphics processing unit) to perform fast com-
putations in software in the early trigger stages. General-purpose computing on
GPUs is emerging as a new paradigm in several fields of science, although so far
applications have been tailored to the specific strengths of such devices, exploiting
parallelization and avoiding real-time applications. With the steady reduction of
GPU latencies, and the increase in link and memory throughputs, the use of such
devices for real-time applications in high-energy physics data acquisition and trigger
systems is becoming ripe.
A pilot project within NA62 aims at integrating GPUs into the central L0 trig-
ger processor, and also to use them as a fast online processors for computing trigger
primitives. Several TDC-equipped sub-detectors with sub-nanosecond time resolu-
tion will participate to the first-level NA62 trigger (L0), fully integrated with the
data-acquisition system, to reduce the readout rate of all sub-detectors to 1 MHz,
using multiplicity information asynchronously computed over time windows of a few
ns, both for positive sub-detectors and for vetos. The online use of GPUs would
allow the computation of more complex trigger primitives already at this first trigger
level. Cheap commercial links can be used to collect trigger primitives, and the task
of the dedicated central processor is to perform time matching of those, generate
the trigger signal and re-align it in time for synchronous distribution.
Most difficulties related to the reconstruction of physical observables used for
trigger purposes can be reconduced to pattern recognition problems. Such issues of
this kind can be treated through parallel algorithms. The fast ellipse recognition in
a two dimensional array, for instance, can be addressed using the generalized Hough
transform approach; the linear interpolation with standard methods can be achieved
at high resolution exploiting the GPU computing power.
We describe the architecture of the proposed system and present the perfor-
mances achieved in tests on a real detector data acquisition system, to perform
online recognition of rings from a RICH detector with sub-nanosecond time resolu-
tion. This information at the trigger level is an essential ingredient for the online
particle identification aming at selecting interesting events with a higher efficiency.
In the future this approach will be extended to the reconstruction of other phys-
ical observables such as the particles’ momenta in tracking detectors or energy clus-
ters in calorimeters.
The challenges and the prospects of this promising idea are discussed.
