arXiv subjects

David Rohr

Publications and source records attributed to David Rohr.

At least 19 recordsLinked to original sources

arXiv2026

Improvements of the ALICE GPU TPC tracking and GPU framework for online and offline processing of Run 3 Pb-Pb data

ALICE is the dedicated heavy ion experiment at the LHC at CERN and records lead-lead collisions at a rate of up to 50 kHz in LHC Run 3. To cope with such collision and data rates, ALICE uses a new GEM TPC with continuous readout and a GPU-based online computing farm for data compression. Operating the first GEM TPC of this size with large space charge distortions due to the high collision rate has many implications for the track reconstruction algorithm, both anticipated and unanticipated. With real Pb-Pb data available, the TPC tracking algorithm needed to be refined, particularly with respect to improved cluster attachment at the inner TPC region. In order to use the online computing farm efficiently for offline processing when there is no beam in the LHC, ALICE is currently running TPC tracking on GPUs also in offline processing. For the future, ALICE aims to run more computing steps on the GPU, and to use other GPU-enabled resources besides its online computing farm. These aspects, along with better possibilities for performance optimizations led to several improvements of the GPU framework and GPU tracking code, particularly using Run Time Compilation (RTC). The talk will give an overview of the improvements for the ALICE tracking code, mostly based on experience from reconstructing real Pb-Pb data with high TPC occupancy. In addition, an overview of the online and offline processing status on GPUs will be given, and an overview of how RTC improves the ALICE tracking code and GPU support.

physics.ins-det↗

arXiv2025

Towards Robust Optimization-Based Autonomous Dynamic Soaring with a Fixed-Wing UAV

Dynamic soaring is a flying technique to exploit the energy available in wind shear layers, enabling potentially unlimited flight without the need for internal energy sources. We propose a framework for autonomous dynamic soaring with a fixed-wing unmanned aerial vehicle (UAV). The framework makes use of an explicit representation of the wind field and a classical approach for guidance and control of the UAV. Robustness to wind field estimation error is achieved by constructing point-wise robust reference paths for dynamic soaring and the development of a robust path following controller for the fixed-wing UAV. Wind estimation and path tracking performance are validated with real flight tests to demonstrate robust path-following in real wind conditions. In simulation, we demonstrate robust dynamic soaring flight subject to varied wind conditions, estimation errors and disturbances. Together, our results strongly indicate the ability of the proposed framework to achieve autonomous dynamic soaring flight in wind shear.

cs.RO↗

arXiv2025

Improvements of the GPU Processing Framework for ALICE

ALICE is the dedicated heavy ion experiment at the LHC at CERN and records lead-lead collisions at a rate of up to 50 kHz. The detector with the highest data rate of up to 3.4 TB/s is the TPC. ALICE performs the full online TPC processing corresponding to more than 95\% of the total workload on GPUs, and when there is no beam in the LHC, the online computing farm's GPUs are used to speed up the offline processing. After the deployment of the first version of the online TPC processing needed for data taking, ALICE has implemented many improvements to its GPU processing framework. These include a run time compilation mode applying on the fly optimizations, improvements to parallelize / speed up the GPU compilation, debugging modes to guarantee reproducible and deterministic results in concurrent reconstruction, and framework features to leverage common components in the code of different detectors. The proceedings give an overview of the ALICE experience with GPUs in online and offline processing and present the latest GPU processing framework features.

physics.ins-det↗

arXiv2025

Safe Periodic Trochoidal Paths for Fixed-Wing UAVs in Confined Windy Environments

Due to their energy-efficient flight characteristics, fixed-wing type UAVs are useful robotic tools for long-range and duration flight applications in large-scale environments. However, flying fixed-wing UAV in confined environments, such as mountainous regions, can be challenging due to their limited maneuverability and sensitivity to uncertain wind conditions. In this work, we first analyze periodic trochoidal paths that can be used to define wind-aware terminal loitering states. We then propose a wind-invariant safe set of trochoidal paths along with a switching strategy for selecting the corresponding minimum-extent periodic path type. Finally, we show that planning with this minimum-extent set allows us to safely reach up to 10 times more locations in mountainous terrain compared to planning with a single, conservative loitering maneuver.

cs.RO↗

arXiv2025

Usage of GPUs for online and offline Reconstruction in ALICE in Run 3

ALICE records Pb-Pb collisions in Run 3 at an unprecedented rate of 50 kHz, storing all data in continuous readout (triggerless) mode. The main purpose of the ALICE online computing farm is the calibration of the detectors and the compression of the recorded data. The detector with the largest data volume by far is the TPC, and the online farm is thus optimized for fast and efficient processing of TPC data during data taking. For this, ALICE leverages heavily the compute power of GPUs. When there is no beam in the LHC, the GPU-equipped farm performs the offline reconstruction of the recorded data, in addition to the GRID. Since the majority of the compute capacity of the farm is in the GPUs, and meanwhile also some GRID sites begin to offer GPU resources, ALICE has started to offload other parts of the offline reconstruction to GPUs as well. The talk will present the experience and processing performance with GPUs in the Run 3 Pb-Pb and pp online and offline processing in ALICE.

physics.ins-det↗

arXiv2024

Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm

Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O$^2$ data processing within a single software framework. The ALICE O$^2$ Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with PbPb collision data at a 50 kHz hadronic interaction rate. Also, the O$^2$ EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings.

hep-ex↗

arXiv2024

The O2 software framework and GPU usage in ALICE online and offline reconstruction in Run 3

ALICE has upgraded many of its detectors for LHC Run 3 to operate in continuous readout mode recording Pb--Pb collisions at 50 kHz interaction rate without trigger. This results in the need to process data in real time at rates 100 times higher than during Run 2. In order to tackle such a challenge we introduced O2, a new computing system and the associated infrastructure. Designed and implemented during the LHC long shutdown 2, O2 is now in production taking care of all the data processing needs of the experiment. O2 is designed around the message passing paradigm, enabling resilient, parallel data processing for both the synchronous (to LHC beam) and asynchronous data taking and processing phases. The main purpose of the synchronous online reconstruction is detector calibration and raw data compression. This synchronous processing is dominated by the TPC detector, which produces by far the largest data volume, and TPC reconstruction runs fully on GPUs. When there is no beam in the LHC, the powerful GPU-equipped online computing farm of ALICE is used for the asynchronous reconstruction, which creates the final reconstructed output for analysis from the compressed raw data. Since the majority of the compute performance of the online farm is in the GPUs, and since the asynchronous processing is not dominated by the TPC in the way the synchronous processing is, there is an ongoing effort to offload a significant amount of compute load from other detectors to the GPU as well.

physics.ins-det↗

arXiv2022

The ALICE Run 3 Online / Offline Processing

The ALICE experiment has undergone a major upgrade for LHC Run 3 and will collect data at an interaction rate 50 times larger than before. The new computing scheme for Run 3 replaces the traditionally separate online and offline frameworks by a unified one, which is called O2. Processing will happen in two phases. During data taking, a synchronous processing phase performs data compression, calibration, and quality control on the online computing farm. The output is stored on an onsite disk buffer. When there is no beam in the LHC, the same computing farm is used for the asynchronous reprocessing of the data which yields the final reconstruction output. The O2 project consists of three main parts:. The Event Processing Nodes (EPN) equipped with GPUs deliver the bulk of the computing capacity and perform the majority of the reconstruction and the calibration. The First Level Processors (FLP) receive the data via optical links from the detectors and perform local processing where it is needed, which can optionally happen in the user logic of the FPGA based readout card. Between the FLP and the EPN farms the data is distributed in the network such that the EPNs receive complete collision data for the processing. The Physics and Data Processing (PDP) group develops the software framework and the reconstruction and calibration algorithms. The current O2 setup is capable of handling in real time the peak data rate foreseen for data taking of Pb--Pb collisions at 50 kHz interaction rate..

physics.ins-det↗

arXiv2021

Usage of GPUs in ALICE Online and Offline processing during LHC Run 3

ALICE will significantly increase its Pb--Pb data taking rate from the 1\,kHz of triggered readout in Run 2 to 50 kHz of continuous readout for LHC Run 3. Updated tracking detectors are installed for Run 3 and a new two-phase computing strategy is employed. In the first synchronous phase during the data taking, the raw data is compressed for storage to an on-site disk buffer and the required data for the detector calibration is collected. In the second asynchronous phase the compressed raw data is reprocessed using the final calibration to produce the final reconstruction output. Traditional CPUs are unable to cope with the huge data rate and processing demands of the synchronous phase, therefore ALICE employs GPUs to speed up the processing. Since the online computing farm performs a part of the asynchronous processing when there is no beam in the LHC, ALICE plans to use the GPUs also for this second phase. This paper gives an overview of the GPU processing in the synchronous phase, the full system test to validate the reference GPU architecture, and the prospects for the GPU usage in the asynchronous phase.

physics.ins-det↗

arXiv2021

ALICE : online-offline processing for Run 3

ALICE will increase the data-taking rate for Run 3 significantly to 50 kHz continuous readout of minimum bias Pb--Pb collisions. The foreseen reconstruction strategy consists of 2 phases: 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, sophisticated compression and removal of TPC data not use 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. In order to achieve the best utilization of the computing farm, we plan to offload also several steps of the asynchronous reconstruction to the GPU. This paper gives an overview of the important processing steps during synchronous and asynchronous reconstruction and of the required computing capabilities.

physics.ins-det↗

arXiv2020

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.

physics.ins-det↗

arXiv2020

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.

physics.ins-det↗

arXiv2020

LQR-Assisted Whole-Body Control of a Wheeled Bipedal Robot with Kinematic Loops

We present a hierarchical whole-body controller leveraging the full rigid body dynamics of the wheeled bipedal robot Ascento. We derive closed-form expressions for the dynamics of its kinematic loops in a way that readily generalizes to more complex systems. The rolling constraint is incorporated using a compact analytic solution based on rotation matrices. The non-minimum phase balancing dynamics are accounted for by including a linear-quadratic regulator as a motion task. Robustness when driving curves is increased by regulating the lean angle as a function of the zero-moment point. The proposed controller is computationally lightweight and significantly extends the rough-terrain capabilities and robustness of the system, as we demonstrate in several experiments.

cs.RO↗

arXiv2019

Global Track Reconstruction and Data Compression Strategy in ALICE for LHC Run 3

In LHC Run 3, ALICE will increase the data taking rate significantly, from an approximately 1 kHz trigger readout in minimum-bias Pb--Pb collisions to a 50 kHz continuous readout rate. The reconstruction strategy of the online-offline computing upgrade foresees a synchronous online reconstruction stage during data taking, which generates the detector calibration, and a posterior calibrated asynchronous reconstruction stage. The huge amount of data requires a significant compression in order to store all recorded events. The aim is a factor 20 compression of the TPC data, which is one of the main challenges during synchronous reconstruction. In addition, the reconstruction will run online, processing 50 times more collisions than at present, yielding results comparable to current offline reconstruction. These requirements pose new challenges for the tracking, including the continuous TPC readout, more overlapping collisions, no a priori knowledge of the primary vertex position and of location-dependent calibration during the synchronous phase, identification of low-momentum looping tracks, and a distorted refit to improve track model entropy coding. At the 2018 workshop, the TPC tracking for Run 3 was presented, which matches the physics performance of the Run 2 offline tracking. It 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. Porting more reconstruction steps like the remainder of the TPC reconstruction and tracking for other detectors to GPU will shift the computing balance from traditional processors towards GPUs. These proceedings focus on the global tracking strategy, including the ITS and TRD detectors, offloading more reconstruction steps onto GPU, and the approaches taken to achieve the necessary data compression.

physics.ins-det↗

arXiv2019

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.

physics.ins-det↗

arXiv2019

Attitude- and Cruise Control of a VTOL Tiltwing UAV

This paper presents the mathematical modeling, controller design, and flight-testing of an over-actuated Vertical Take-off and Landing (VTOL) tiltwing Unmanned Aerial Vehicle (UAV). Based on simplified aerodynamics and first-principles, a dynamical model of the UAV is developed which captures key aerodynamic effects including propeller slipstream on the wing and post-stall characteristics of the airfoils. The model-based steady-state flight envelope and the corresponding trim-actuation is analyzed and the overactuation of the UAV solved by optimizing for, e.g., power-optimal trims. The developed control system is composed of two controllers: First, a low-level attitude controller based on dynamic inversion and a daisy-chaining approach to handle allocation of redundant actuators. Secondly, a higher-level cruise controller to track a desired vertical velocity. It is based on a linearization of the system and look-up tables to determine the strong and nonlinear variation of the trims throughout the flight-envelope. We demonstrate the performance of the control-system for all flight phases (hover, transition, cruise) in extensive flight-tests.

eess.SY↗

arXiv2018

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.

physics.ins-det↗

arXiv2018

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.

physics.ins-det↗