Corchia, Federico Andrea Guillaume
 
(2022)
Deployment of a data analysis workflow of the ATLAS experiment on HPC systems.
[Laurea magistrale], Università di Bologna, Corso di Studio in 
Physics [LM-DM270]
   
  
  
        
        
	
  
  
  
  
  
  
  
    
  
    
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      Abstract
      LHC experiments produce an enormous amount of data, estimated of the order of a few PetaBytes per year. Data management takes place using the Worldwide LHC Computing Grid (WLCG) grid infrastructure, both for storage and processing operations. However, in recent years, many more resources are available on High Performance Computing (HPC) farms, which generally have many computing nodes with a high number of processors. Large collaborations are working to use these resources in the most efficient way, compatibly with the constraints imposed by computing models (data distributed on the Grid, authentication, software dependencies, etc.).
The aim of this thesis project is to develop a software framework that allows users to process a typical data analysis workflow of the ATLAS experiment on HPC systems. The developed analysis framework shall be deployed on the computing resources of the Open Physics Hub project and on the CINECA Marconi100 cluster, in view of the switch-on of the Leonardo supercomputer, foreseen in 2023.
     
    
      Abstract
      LHC experiments produce an enormous amount of data, estimated of the order of a few PetaBytes per year. Data management takes place using the Worldwide LHC Computing Grid (WLCG) grid infrastructure, both for storage and processing operations. However, in recent years, many more resources are available on High Performance Computing (HPC) farms, which generally have many computing nodes with a high number of processors. Large collaborations are working to use these resources in the most efficient way, compatibly with the constraints imposed by computing models (data distributed on the Grid, authentication, software dependencies, etc.).
The aim of this thesis project is to develop a software framework that allows users to process a typical data analysis workflow of the ATLAS experiment on HPC systems. The developed analysis framework shall be deployed on the computing resources of the Open Physics Hub project and on the CINECA Marconi100 cluster, in view of the switch-on of the Leonardo supercomputer, foreseen in 2023.
     
  
  
    
    
      Tipologia del documento
      Tesi di laurea
(Laurea magistrale)
      
      
      
      
        
      
        
          Autore della tesi
          Corchia, Federico Andrea Guillaume
          
        
      
        
          Relatore della tesi
          
          
        
      
        
          Correlatore della tesi
          
          
        
      
        
          Scuola
          
          
        
      
        
          Corso di studio
          
          
        
      
        
          Indirizzo
          NUCLEAR AND SUBNUCLEAR PHYSICS
          
        
      
        
      
        
          Ordinamento Cds
          DM270
          
        
      
        
          Parole chiave
          High Energy Physics,High Performance Computing,Grid Computing,ATLAS
          
        
      
        
          Data di discussione della Tesi
          15 Luglio 2022
          
        
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Tesi di laurea
(NON SPECIFICATO)
      
      
      
      
        
      
        
          Autore della tesi
          Corchia, Federico Andrea Guillaume
          
        
      
        
          Relatore della tesi
          
          
        
      
        
          Correlatore della tesi
          
          
        
      
        
          Scuola
          
          
        
      
        
          Corso di studio
          
          
        
      
        
          Indirizzo
          NUCLEAR AND SUBNUCLEAR PHYSICS
          
        
      
        
      
        
          Ordinamento Cds
          DM270
          
        
      
        
          Parole chiave
          High Energy Physics,High Performance Computing,Grid Computing,ATLAS
          
        
      
        
          Data di discussione della Tesi
          15 Luglio 2022
          
        
      
      URI
      
      
     
   
  
  
  
  
  
    
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