1,175 research outputs found
Data processing and online reconstruction
In the upcoming upgrades for Run 3 and 4, the LHC will significantly increase
Pb--Pb and pp interaction rates. This goes along with upgrades of all
experiments, ALICE, ATLAS, CMS, and LHCb, related to both the detectors and the
computing. The online processing farms must employ faster, more efficient
reconstruction algorithms to cope with the increased data rates, and data
compression factors must increase to fit the data in the affordable capacity
for permanent storage. Due to different operating conditions and aims, the
experiments follow different approaches, but there are several common trends
like more extensive online computing and the adoption of hardware accelerators.
This paper gives an overview and compares the data processing approaches and
the online computing farms of the LHC experiments today in Run 2 and for the
upcoming LHC Run 3 and 4.Comment: 6 pages, 0 figures, contribution to LHCP2018 conferenc
Overview of online and offline reconstruction in ALICE for LHC Run 3
In LHC Run 3, ALICE will increase the data taking rate significantly to 50
kHz continuous readout of minimum bias Pb--Pb collisions. The reconstruction
strategy of the online-offline computing upgrade foresees a first synchronous
online reconstruction stage during data taking enabling detector calibration,
and a posterior calibrated asynchronous reconstruction stage. The main
challenges include processing and compression of 50 times more events per
second than in Run 2, identification of removable TPC tracks and hits not used
for physics, tracking of TPC data in continuous readout, the TPC space-charge
distortion calibrations, and in general running more reconstruction steps
online compared to Run 2. ALICE will leverage GPUs to facilitate the
synchronous processing with the available resources. For the best GPU resource
utilization, we plan to offload also several steps of the asynchronous
reconstruction to the GPU. In order to be vendor independent, we support CUDA,
OpenCL, and HIP, and we maintain a common C++ source code that also runs on the
CPU. We will give an overview of the global reconstruction and tracking
strategy, a comparison of the performance on CPU and different GPU models. We
will discuss the scaling of the reconstruction with the input data size, as
well as estimates of the required resources in terms of memory and processing
power.Comment: 8 pages, 3 figures, proceedings of Connecting the Dots 2020 Worksho
GPU-based reconstruction and data compression at ALICE during LHC Run 3
In LHC Run 3, ALICE will increase the data taking rate significantly to 50
kHz continuous read out of minimum bias Pb-Pb collisions. The reconstruction
strategy of the online offline computing upgrade foresees a first synchronous
online reconstruction stage during data taking enabling detector calibration,
and a posterior calibrated asynchronous reconstruction stage. The significant
increase in the data rate poses challenges for online and offline
reconstruction as well as for data compression. Compared to Run 2, the online
farm must process 50 times more events per second and achieve a higher data
compression factor. ALICE will rely on GPUs to perform real time processing and
data compression of the Time Projection Chamber (TPC) detector in real time,
the biggest contributor to the data rate. With GPUs available in the online
farm, we are evaluating their usage also for the full tracking chain during the
asynchronous reconstruction for the silicon Inner Tracking System (ITS) and
Transition Radiation Detector (TRD). The software is written in a generic way,
such that it can also run on processors on the WLCG with the same
reconstruction output. We give an overview of the status and the current
performance of the reconstruction and the data compression implementations on
the GPU for the TPC and for the global reconstruction.Comment: 7 pages, 4 figures, proceedings of CHEP 2019 conferenc
GPU-based Online Track Reconstruction for the ALICE TPC in Run 3 with Continuous Read-Out
In LHC Run 3, ALICE will increase the data taking rate significantly to 50
kHz continuous read-out of minimum bias Pb-Pb collisions. The reconstruction
strategy of the online-offline computing upgrade foresees a first synchronous
online reconstruction stage during data taking enabling detector calibration
and data compression, and a posterior calibrated asynchronous reconstruction
stage. Many new challenges arise, among them continuous TPC read-out, more
overlapping collisions, no a priori knowledge of the primary vertex and of
location-dependent calibration in the synchronous phase, identification of
low-momentum looping tracks, and sophisticated raw data compression. The
tracking algorithm for the Time Projection Chamber (TPC) will be based on a
Cellular Automaton and the Kalman filter. The reconstruction shall run online,
processing 50 times more collisions per second than today, while yielding
results comparable to current offline reconstruction. Our TPC track finding
leverages the potential of hardware accelerators via the OpenCL and CUDA APIs
in a shared source code for CPUs and GPUs for both reconstruction stages. We
give an overview of the status of Run 3 tracking including performance on
processors and GPUs and achieved compression ratios.Comment: 8 pages, 7 figures, contribution to CHEP 2018 conferenc
Track Reconstruction in the ALICE TPC using GPUs for LHC Run 3
In LHC Run 3, ALICE will increase the data taking rate significantly to
continuous readout of 50 kHz minimum bias Pb-Pb collisions. The reconstruction
strategy of the online offline computing upgrade foresees a first synchronous
online reconstruction stage during data taking enabling detector calibration,
and a posterior calibrated asynchronous reconstruction stage. We present a
tracking algorithm for the Time Projection Chamber (TPC), the main tracking
detector of ALICE. The reconstruction must yield results comparable to current
offline reconstruction and meet the time constraints like in the current High
Level Trigger (HLT), processing 50 times as many collisions per second as
today. It is derived from the current online tracking in the HLT, which is
based on a Cellular automaton and the Kalman filter, and we integrate missing
features from offline tracking for improved resolution. The continuous TPC
readout and overlapping collisions pose new challenges: conversion to spatial
coordinates and the application of time- and location dependent calibration
must happen in between of track seeding and track fitting while the TPC
occupancy increases five-fold. The huge data volume requires a data reduction
factor of 20, which imposes additional requirements: the momentum range must be
extended to identify low-pt looping tracks and a special refit in uncalibrated
coordinates improves the track model entropy encoding. Our TPC track finding
leverages the potential of hardware accelerators via the OpenCL and CUDA APIs
in a shared source code for CPUs, GPUs, and both reconstruction stages. Porting
more reconstruction steps like the remainder of the TPC reconstruction and
tracking for other detectors will shift the computing balance from traditional
processors to GPUs.Comment: 13 pages, 10 figures, proceedings to Connecting The Dots Workshop,
Seattle, 201
Fast TPC Online Tracking on GPUs and Asynchronous Data Processing in the ALICE HLT to facilitate Online Calibration
ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at
the Large Hadron Collider (LHC) at CERN, which is today the most powerful
particle accelerator worldwide. The High Level Trigger (HLT) is an online
compute farm of about 200 nodes, which reconstructs events measured by the
ALICE detector in real-time. The HLT uses a custom online data-transport
framework to distribute data and workload among the compute nodes. ALICE
employs several calibration-sensitive subdetectors, e.g. the TPC (Time
Projection Chamber). For a precise reconstruction, the HLT has to perform the
calibration online. Online-calibration can make certain Offline calibration
steps obsolete and can thus speed up Offline analysis. Looking forward to ALICE
Run III starting in 2020, online calibration becomes a necessity. The main
detector used for track reconstruction is the TPC. Reconstructing the
trajectories in the TPC is the most compute-intense step during event
reconstruction. Therefore, a fast tracking implementation is of great
importance. Reconstructed TPC tracks build the basis for the calibration making
a fast online-tracking mandatory. We present several components developed for
the ALICE High Level Trigger to perform fast event reconstruction and to
provide features required for online calibration. As first topic, we present
our TPC tracker, which employs GPUs to speed up the processing, and which bases
on a Cellular Automaton and on the Kalman filter. Our TPC tracking algorithm
has been successfully used in 2011 and 2012 in the lead-lead and the
proton-lead runs. We have improved it to leverage features of newer GPUs and we
have ported it to support OpenCL, CUDA, and CPUs with a single common source
code. This makes us vendor independent. As second topic, we present framework
extensions required for online calibration. ...Comment: 8 pages, 6 figures, contribution to CHEP 2015 conferenc
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images
In cryo-electron microscopy (EM), molecular structures are determined from
large numbers of projection images of individual particles. To harness the full
power of this single-molecule information, we use the Bayesian inference of EM
(BioEM) formalism. By ranking structural models using posterior probabilities
calculated for individual images, BioEM in principle addresses the challenge of
working with highly dynamic or heterogeneous systems not easily handled in
traditional EM reconstruction. However, the calculation of these posteriors for
large numbers of particles and models is computationally demanding. Here we
present highly parallelized, GPU-accelerated computer software that performs
this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI
parallelization combined with both CPU and GPU computing. The resulting BioEM
software scales nearly ideally both on pure CPU and on CPU+GPU architectures,
thus enabling Bayesian analysis of tens of thousands of images in a reasonable
time. The general mathematical framework and robust algorithms are not limited
to cryo-electron microscopy but can be generalized for electron tomography and
other imaging experiments
Online Calibration of the TPC Drift Time in the ALICE High Level Trigger
ALICE (A Large Ion Collider Experiment) is one of four major experiments at
the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is a
compute cluster, which reconstructs collisions as recorded by the ALICE
detector in real-time. It employs a custom online data-transport framework to
distribute data and workload among the compute nodes.
ALICE employs subdetectors sensitive to environmental conditions such as
pressure and temperature, e.g. the Time Projection Chamber (TPC). A precise
reconstruction of particle trajectories requires the calibration of these
detectors. Performing the calibration in real time in the HLT improves the
online reconstructions and renders certain offline calibration steps obsolete
speeding up offline physics analysis. For LHC Run 3, starting in 2020 when data
reduction will rely on reconstructed data, online calibration becomes a
necessity. Reconstructed particle trajectories build the basis for the
calibration making a fast online-tracking mandatory. The main detectors used
for this purpose are the TPC and ITS (Inner Tracking System). Reconstructing
the trajectories in the TPC is the most compute-intense step.
We present several improvements to the ALICE High Level Trigger developed to
facilitate online calibration. The main new development for online calibration
is a wrapper that can run ALICE offline analysis and calibration tasks inside
the HLT. On top of that, we have added asynchronous processing capabilities to
support long-running calibration tasks in the HLT framework, which runs
event-synchronously otherwise. In order to improve the resiliency, an isolated
process performs the asynchronous operations such that even a fatal error does
not disturb data taking. We have complemented the original loop-free HLT chain
with ZeroMQ data-transfer components. [...]Comment: 8 pages, 10 figures, proceedings to 2016 IEEE-NPSS Real Time
Conferenc
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