Analyzing as many as one billion proton collisions per second or tens of
thousands of very complex lead collisions is not an easy job for a
traditional computer farm. With the latest upgrades of the LHC experiments
due to come into action next year, their demand for data processing
potential has significantly increased. As their new computational challenges
might not be met using traditional central processing units (CPUs), the four
large experiments are adopting graphics processing units (GPUs).
GPUs are highly efficient processors, specialized in image processing, and
were originally designed to accelerate the rendering of three-dimensional
computer graphics. Their use has been studied in the past couple of years by
the LHC experiments, the Worldwide LHC Computing Grid (WLCG) and CERN
openlab. Increasing the use of GPUs in high-energy physics will improve not
only the quality and size of the computing infrastructure, but also the
overall energy efficiency.
"The LHC's ambitious upgrade program poses a range of exciting computing
challenges; GPUs can play an important role in supporting machine-learning
approaches to tackling many of these," says Enrica Porcari, Head of the CERN
IT department. "Since 2020, the CERN IT department has provided access to
GPU platforms in the data center, which have proven popular for a range of
applications. On top of this, CERN openlab is carrying out important
investigations into the use of GPUs for machine learning through
collaborative R&D projects with industry and the Scientific Computing
Collaborations group is working to help port—and optimize—key code from the
experiments."
ALICE has pioneered the use of GPUs in its high-level trigger online
computer farm (HLT) since 2010 and is the only experiment using them to such
a large extent to date. The newly upgraded ALICE detector has more than 12
billion electronic sensor elements that are read out continuously, creating
a data stream of more than 3.5 terabytes per second. After first-level data
processing, there remains a stream of up to 600 gigabytes per second. These
data are analyzed online on a high-performance computer farm, implementing
250 nodes, each equipped with eight GPUs and two 32-core CPUs. Most of the
software that assembles individual particle detector signals into particle
trajectories (event reconstruction) has been adapted to work on GPUs.
In particular, the GPU-based online reconstruction and compression of the
data from the Time Projection Chamber, which is the largest contributor to
the data size, allows ALICE to further reduce the rate to a maximum of 100
gigabytes per second before writing the data to the disk. Without GPUs,
about eight times as many servers of the same type and other resources would
be required to handle the online processing of lead collision data at a 50
kHz interaction rate.
ALICE successfully employed online reconstruction on GPUs during the LHC
pilot beam data taking at the end of October 2021. When there is no beam in
the LHC, the online computer farm is used for offline reconstruction. In
order to leverage the full potential of the GPUs, the full ALICE
reconstruction software has been implemented with GPU support, and more than
80% of the reconstruction workload will be able to run on the GPUs.
From 2013 onwards, LHCb researchers carried out R&D work into the use of
parallel computing architectures, most notably GPUs, to replace parts of the
processing that would traditionally happen on CPUs. This work culminated in
the Allen project, a complete first-level real-time processing implemented
entirely on GPUs, which is able to deal with LHCb's data rate using only
around 200 GPU cards. Allen allows LHCb to find charged particle
trajectories from the very beginning of the real-time processing, which are
used to reduce the data rate by a factor of 30–60 before the detector is
aligned and calibrated and a more complete CPU-based full detector
reconstruction is executed. Such a compact system also leads to substantial
energy efficiency savings.
Starting in 2022, the LHCb experiment will process 4 terabytes of data per
second in real time, selecting 10 gigabytes of the most interesting LHC
collisions each second for physics analysis. LHCb's unique approach is that
instead of offloading work, it will analyze the full 30 million
particle-bunch crossings per second on GPUs.
Together with improvements to its CPU processing, LHCb has also gained
almost a factor of 20 in the energy efficiency of its detector
reconstruction since 2018. LHCb researchers are now looking forward to
commissioning this new system with the first data of 2022, and building on
it to enable the full physics potential of the upgraded LHCb detector to be
realized.
CMS reconstructed LHC collision data with GPUs for the first time during the
LHC pilot beams in October last year. During the first two runs of the LHC,
the CMS HLT ran on a traditional computer farm comprising over 30 000 CPU
cores. However, as the studies for the Phase 2 upgrade of CMS have shown,
the use of GPUs will be instrumental in keeping the cost, size and power
consumption of the HLT farm under control at higher LHC luminosity. And in
order to gain experience with a heterogeneous farm and the use of GPUs in a
production environment, CMS will equip the whole HLT with GPUs from the
start of Run 3: the new farm will be comprised of a total of 25 600 CPU
cores and 400 GPUs.
The additional computing power provided by these GPUs will allow CMS not
only to improve the quality of the online reconstruction but also to extend
its physics program, running the online data scouting analysis at a much
higher rate than before. Today about 30% of the HLT processing can be
offloaded to GPUs: the calorimeters local reconstruction, the pixel tracker
local reconstruction, the pixel-only track and vertex reconstruction. The
number of algorithms that can run on GPUs will grow during Run 3, as other
components are already under development.
ATLAS is engaged in a variety of R&D projects towards the use of GPUs
both in the online trigger system and more broadly in the experiment. GPUs
are already used in many analyses; they are particularly useful for machine
learning applications where training can be done much more quickly. Outside
of machine learning, ATLAS R&D efforts have focused on improving the
software infrastructure in order to be able to make use of GPUs or other
more exotic processors that might become available in a few years. A few
complete applications, including a fast calorimeter simulation, also now run
on GPUs, which will provide the key examples with which to test the
infrastructure improvements.
"All these developments are occurring against a backdrop of unprecedented
evolution and diversification of computing hardware. The skills and
techniques developed by CERN researchers while learning how to best utilize
GPUs are the perfect platform from which to master the architectures of
tomorrow and use them to maximize the physics potential of current and
future experiments," says Vladimir Gligorov, who leads LHCb's Real Time
Analysis project.
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