Back to Search Start Over

Online data compression in the ALICE O$^2$ facility

Authors :
Richter, Matthias Rudolph
Aamodt, Kenneth
Arsene, Ionut Christian
Bravina, Larissa
Dordic, Olja
Eyyubova, Gyulnara
Hille, Per Thomas
Kolevatov, Rodion
Kværnø, Henning
Lindal, Svein
Løvhøiden, Gunnar
Milosevic, Jovan
Nilsson, Mads Stormo
Nyiri, Agnes
Skaali, Toralf Bernhard
Tveter, Trine Spedstad
Tywoniuk, Konrad
Wikne, Jon Christopher
Zabrodin, Evgeny
Alme, Johan
Bablok, Sebastian Robert
Djuvsland, Øystein
Fehlker, Dominik
Haaland, Øystein Senneset
Huang, Meidana
Kanaki, Kalliopi
Klovning, Arne
Larsen, Dag Toppe
Liu, Lijiao
Nystrand, Joakim
Øvrebekk, Gaute
Pommeresche, Bjørn-Erling
Skjerdal, Kyrre
Ullaland, Kjetil
Wagner, Boris
Helstrup, Håvard
Hetland, Kristin Fanebust
Kileng, Bjarte
Røed, Ketil
Pocheptsov, Timur Anatolievich
Altinpinar, Sedat
Røhrich, Dieter
Adamová, Dagmar
Aggarwal, Madan M.
Aglieri Rinella, Gianluca
Ahammed, Zubayer
Ahn, Sang Un
Akindinov, Alexander
Aleksandrov, Dimitry
Alessandro, Bruno
ALICE, Collaboration
Source :
Journal of Physics, Conference Series, 898:032049
Publication Year :
2017

Abstract

The ALICE Collaboration and the ALICE O2 project have carried out detailed studies for a new online computing facility planned to be deployed for Run 3 of the Large Hadron Collider (LHC) at CERN. Some of the main aspects of the data handling concept are partial reconstruction of raw data organized in so called time frames, and based on that information reduction of the data rate without significant loss in the physics information. A production solution for data compression has been in operation for the ALICE Time Projection Chamber (TPC) in the ALICE High Level Trigger online system since 2011. The solution is based on reconstruction of space points from raw data. These so called clusters are the input for reconstruction of particle trajectories. Clusters are stored instead of raw data after a transformation of required parameters into an optimized format and subsequent lossless data compression techniques. With this approach, a reduction of 4.4 has been achieved on average. For Run 3, not only a significantly higher reduction is required but also improvements in the implementation of the actual algorithms. The innermost operations of the processing loop effectively need to be called up to O \(10^{11}\) /s to cope with the data rate. This can only be achieved in a parallel scheme and makes these operations candidates for optimization. The potential of template programming and static dispatch in a polymorphic implementation has been studied as an alternative to the commonly used dynamic dispatch at runtime. In this contribution we report on the development of a specific programming technique to efficiently combine compile time and runtime domains and present results for the speedup of the algorithm. publishedVersion

Details

Language :
English
ISSN :
17426588
Database :
OpenAIRE
Journal :
Journal of Physics, Conference Series, 898:032049
Accession number :
edsair.doi.dedup.....cba181a8bc077cfd43805b7e3a384499