scholarly journals GPU-based reconstruction and data compression at ALICE during LHC Run 3

2020 ◽  
Vol 245 ◽  
pp. 10005
Author(s):  
David Rohr
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Tracking System ◽  
Radiation Detector ◽  
Data Rate ◽  
Transition Radiation Detector ◽  
Real Time Processing ◽  
The Status ◽  
Time Projection ◽  
Online Reconstruction

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.

scholarly journals Gpu-Based Online Track Reconstruction for the Alice Tpc in Run 3 With Continuous Read-Out

2019 ◽  
Vol 214 ◽  
pp. 01050 ◽  
Author(s):  
David Rohr ◽  
Sergey Gorbunov ◽  
Schmidt Ole Marten ◽  
Ruben Shahoyan
Keyword(s):  
Data Compression ◽  
A Priori ◽  
Hardware Accelerators ◽  
Phase Identification ◽  
Track Reconstruction ◽  
Online Processing ◽  
The Status ◽  
Reconstruction Strategy ◽  
Time Projection ◽  
Online Reconstruction

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.

scholarly journals Space-point calibration of the ALICE TPC with track residuals

2020 ◽  
Vol 245 ◽  
pp. 01003
Author(s):  
Marten Ole Schmidt
Keyword(s):  
Tracking System ◽  
Radiation Detector ◽  
Drift Time ◽  
Calibration Procedure ◽  
Tracking Algorithm ◽  
Alice Experiment ◽  
Transition Radiation Detector ◽  
Online Tracking ◽  
Time Projection ◽  
Performance Results

In the LHC Run 3, starting in 2021, the upgraded Time Projection Chamber (TPC) of the ALICE experiment will record minimum bias Pb–Pb collisions in a continuous readout mode at an interaction rate up to 50 kHz. This corresponds to typically 4-5 overlapping collisions during the electron drift time in the detector. Despite careful tuning of the new quadruple GEM-based readout chambers, which fulfill the design requirement of an ion backflow below 1%, these conditions will lead to space-charge distortions of several centimeters that fluctuate in time. They will be corrected via a calibration procedure that uses the information of the Inner Tracking System (ITS), which is located inside, and the Transition Radiation Detector (TRD) and Time-Of-Flight system (TOF), located around the TPC, respectively. By using such a procedure the intrinsic track resolution of the TPC of a few hundred micrometers can be restored. The required online tracking algorithm for the TRD, which is based on a Kalman filter, is presented. The procedure matches extrapolated ITS-TPC tracks to TRD space-points utilizing GPUs. Subsequently these global tracks are refitted neglecting the TPC information. The residuals of the TPC clusters to the interpolation of the refitted tracks are used to create a map of spacecharge distortions. Regular updates of the map compensate for changes in the TPC conditions. The map is applied in the final reconstruction of the data. First performance results of the tracking algorithm will be shown.

scholarly journals Tracking Women During Threat Using GPS and GSM Module

2021 ◽  
Vol 9 (8) ◽  
pp. 2332-2337
Author(s):  
Yerraguntla Preetham Reddy
Keyword(s):  
Real Time ◽  
Mobile Communications ◽  
Public Transportation ◽  
Cell Phone ◽  
Tracking System ◽  
Business Owners ◽  
Geographical Information ◽  
The Status ◽  
A Cell ◽  
Transportation Research

Abstract: During a threat, tracking men/women from any location at any time is considered as extremely beneficial. Using the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technology, a real-time Google map and Arduino based tracking system is implemented. Geographic coordinates are provided by the GPS module periodically. When a person's location is transmitted, the GSM module sends the latitude and longitude of that location to their cell phone. Finally, a cell phone shows the place's name and location via Google map. This would enable owners/users to monitor moving people/vehicles using their cell phones. This research presents experimental results in order to demonstrate the system's feasibility and effectiveness. In spite of GPS technology's excellent accuracy, it's not always applicable due to technical restrictions, for instance limiting participants' views of the satellites when using public transportation, which is crucial. However, GSM is less accurate in terms of spatial accuracy. Incorporating both technologies could be the key to tracking individual's geographical information (origin, route, destination) in a more comprehensive way. Transportation research can be supported by both kinds of tracking technologies in numerous ways. A GPS/GSM system can be used to track women or children 24/7 respectively to interview them on site in real time. This system may also be employed on vehicles in order to prevent theft. This tracking system works for both business owners and individuals wanting to keep track of their fleets or to keep track of expensive assets in the field without having to be there physically. The vehicle's location (Latitude and Longitude) is communicated continuously from a remote location by means of a GSM modem. The GSM modem automatically returns a realtime latitude and longitude coordinates as a response to that particular mobile phone when a request by the user reaches the number in the GSM modem. On demand, this system will continuously monitor the status of a vehicle in motion.

scholarly journals Live GPS Location Tracking using GPRS

2021 ◽  
Vol 9 (VI) ◽  
pp. 744-748
Author(s):  
Jayesh Sharma
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Cloud Services ◽  
Location Tracking ◽  
Location Data ◽  
Satellite Technology ◽  
The Status ◽  
Real Time Tracking ◽  
Arduino Uno ◽  
Tracking Device

In this paper, a real time tracking system is put forward. In this we are going to design a system which is used for tracking and positioning by using (GPS) and (GSM). This design is based on embedded application, which will regularly monitor location and report the status. This tracking device which is used in real time vehicle location tracking is done using the Arduino Uno Atmega328P, SIM800A module and NEO 6M GPS module. For doing so, the Arduino Uno Atmega328P is combined serially to a GSM module and GPS module. The design make use of RS-232 protocol for serial communication between the modems and the microcontroller. A serial driver IC is used for transforming TTL voltage levels to RS-232 voltage levels. The GSM module is used to regularly send the position of the vehicle from distant place. The GPS module that makes use of satellite technology for its navigation system will regularly give information like longitude, latitude, speed, distance travelled etc. For this purpose, Amazon Cloud Services is used for location data handling. The MySQL database is used to reserve all the data of the GPS.

GPU Acceleration of Algorithm to Design Directional Volumetric Display for Real-time Processing

2019 ◽  
pp. 989
Author(s):  
Daiki Matsumoto ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  
Keyword(s):  
Real Time ◽  
Gpu Acceleration ◽  
Real Time Processing ◽  
Time Processing ◽  
Volumetric Display

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

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