Speaker
Antonio Sidoti
(Universita e INFN, Roma I (IT))
Description
General Purpose Graphical Processing Units (GPGPU) provide exceptional
massive parallel computing power with small power consumption. GPGPU
bring high performance computing with off-the-shelf products. However
the full exploitation of this new computing paradigm will not be
possible if software applications only partially employs massive
parallelism.
High Energy Physics experiments have much to gain adopting this new
computing paradigm. In fact,
the expected gain in performance both in reducing the application
latency or in dealing with the data high throughput increase will allow to
employ systems based on GPGPU for data acquisition increasing the
available computing power with smaller electric power consumption. All
these features are very interesting for using GPGPU at trigger level in an
on-line environment to provide fast decision and high rejection power.
In view of possible applications in a trigger system we will show,
using realistic examples based on data from current LHC High
Energy Physics experiments, the improvement in performance of typical
HEP intensive computing applications after tuning and optimization for
running on GPGPU. The methodology to improve the
performance will also be shown together with results using different
GPGPU architectures.
In particular the porting to GPGPU architecture of two typical HEP
reconstruction algorithms will be shown: tracking in the inner detector and
jet clustering in the calorimeter.
Tracking in the very dense LHC environment is very challenging with
multiple minimum bias interactions superimposed to the high transfered
momentum one. Both pattern recognition and track fitting would benefit
from massive parallelism for high troughput processing that can be
fully exploited at trigger level.
Also jet clustering with the very fine granularity of LHC experiments
calorimetry would profit from the massive parallelism offered by GPGPU.
Primary author
Antonio Sidoti
(Universita e INFN, Roma I (IT))
Co-authors
Alessandro Gabrielli
(Universita e INFN (IT))
Franco Semeria
(Universita e INFN (IT))
Lorenzo Rinaldi
(Universita e INFN (IT))
Matteo Negrini
(Universita e INFN (IT))
Mr
Mauro Belgiovine
(INFN BOlogna)
Prof.
Mauro Villa
(University of Bologna)
Riccardo Di Sipio
(Universita e INFN (IT))