We present a fast general-purpose algorithm for high-throughput clustering of
data "with a two dimensional organization". The algorithm is designed to be
implemented with FPGAs or custom electronics. The key feature is a processing
time that scales linearly with the amount of data to be processed. This means
that clustering can be performed in pipeline with the readout, without
suffering from combinatorial delays due to looping multiple times through all
the data. This feature makes this algorithm especially well suited for problems
where the data has high density, e.g. in the case of tracking devices working
under high-luminosity condition such as those of LHC or Super-LHC. The
algorithm is organized in two steps: the first step (core) clusters the data;
the second step analyzes each cluster of data to extract the desired
information. The current algorithm is developed as a clustering device for
modern high-energy physics pixel detectors. However, the algorithm has much
broader field of applications. In fact, its core does not specifically rely on
the kind of data or detector it is working for, while the second step can and
should be tailored for a given application. Applications can thus be foreseen
to other detectors and other scientific fields ranging from HEP calorimeters to
medical imaging. An additional advantage of this two steps approach is that the
typical clustering related calculations (second step) are separated from the
combinatorial complications of clustering. This separation simplifies the
design of the second step and it enables it to perform sophisticated
calculations achieving online-quality in online applications. The algorithm is
general purpose in the sense that only minimal assumptions on the kind of
clustering to be performed are made.Comment: 11th Frontier Detectors For Frontier Physics conference (2009
