scholarly journals The upgrade of the ATLAS High Level Trigger and data acquisition systems and their integration

2014 ◽  
Author(s):  
Ricardo Abreu
Keyword(s):  
Data Acquisition ◽  
Data Acquisition Systems ◽  
Level Trigger ◽  
High Level

scholarly journals ATLAS Sim@P1 upgrades during long shutdown two

2020 ◽  
Vol 245 ◽  
pp. 07044
Author(s):  
Frank Berghaus ◽  
Franco Brasolin ◽  
Alessandro Di Girolamo ◽  
Marcus Ebert ◽  
Colin Roy Leavett-Brown ◽  
...  
Keyword(s):  
Large Hadron Collider ◽  
Data Acquisition ◽  
Process Simulation ◽  
Hadron Collider ◽  
Simulation Data ◽  
Level Trigger ◽  
Offline Processing ◽  
High Level

The Simulation at Point1 (Sim@P1) project was built in 2013 to take advantage of the ATLAS Trigger and Data Acquisition High Level Trigger (HLT) farm. The HLT farm provides around 100,000 cores, which are critical to ATLAS during data taking. When ATLAS is not recording data, such as the long shutdowns of the LHC, this large compute resource is used to generate and process simulation data for the experiment. At the beginning of the second long shutdown of the large hadron collider, the HLT farm including the Sim@P1 infrastructure was upgraded. Previous papers emphasised the need for simple, reliable, and efficient tools and assessed various options to quickly switch between data acquisition operation and offline processing. In this contribution, we describe the new mechanisms put in place for the opportunistic exploitation of the HLT farm for offline processing and give the results from the first months of operation.

scholarly journals Sim@P1: Using Cloudscheduler for offline processing on the ATLAS HLT farm

2019 ◽  
Vol 214 ◽  
pp. 07021
Author(s):  
Frank Berghaus ◽  
Franco Brasolin ◽  
Kevin Casteels ◽  
Colson Driemel ◽  
Marcus Ebert ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Virtual Machines ◽  
Simulation Data ◽  
Atlas Experiment ◽  
Level Trigger ◽  
Technical Details ◽  
Offline Processing ◽  
High Level ◽  
And Control ◽  
Cloud Resources

The Simulation at Point1 (Sim@P1) project was established in 2013 to take advantage of the Trigger and Data Acquisition High Level Trigger (HLT) farm of the ATLAS experiment at the LHC. The HLT farm is a significant compute resource, which is critical to ATLAS during data taking. This large compute resource is used to generate and process simulation data for the experiment when ATLAS is not recording data. The Sim@P1 system uses virtual machines, deployed by OpenStack, in order to isolate the resources from the ATLAS technical and control network. During the upcoming long shutdown in 2019 (LS2), the HLT farm including the Sim@P1 infrastructure will be upgraded. A previous paper on the project emphasized the need for “simple, reliable, and efficient tools” to quickly switch between data acquisition operation and offline processing. In this contribution we assess various options for updating and simplifying the provisional tools. Cloudscheduler is a tool for provisioning cloud resources for batch computing that has been managing cloud resources in HEP offline computing since 2012. We present the argument for choosing Cloudscheduler, and describe technical details regarding optimal utilization of the Sim@P1 re-sources.

scholarly journals The LHCb Data Acquisition and High Level Trigger Processing Architecture

2015 ◽  
Vol 664 (8) ◽  
pp. 082011 ◽  
Author(s):  
M. Frank ◽  
C. Gaspar ◽  
B. Jost ◽  
N. Neufeld
Keyword(s):  
Data Acquisition ◽  
Level Trigger ◽  
High Level ◽  
Processing Architecture

A DSP based FASTBUS board for data acquisition or high level trigger control in DELPHI electromagnetic calorimeters

1989 ◽  
Vol 36 (5) ◽  
pp. 1469-1474 ◽  
Author(s):  
F. Bertolino ◽  
F. Bianchi ◽  
R. Cirio ◽  
M.P. Clara ◽  
D. Crosetto ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Level Trigger ◽  
High Level ◽  
Electromagnetic Calorimeters

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

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