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Computing and Software - Public Results

Introduction

On this page public results and material concerning ATLAS Computing and Software is collected...

Computing TDR and related Documents

report links year
Update of Computing Models for Run-2 CDS 2014
Computing TDR CDS 2005
Computing Model Document CDS 2004
Technical Proposal CDS 1996

Public Notes, Conference Proceedings and Slides

type of public documents links
list of ATLAS-SOFT-PUB notes CDS
list of conference proceedings CDS
list of conference talks CDS

Recent Public Plots

The dependency of reconstruction wall time per event on the average number of interactions per bunch crossing is shown. The plot contains a selection of Tier-0 jobs reconstructing RAW data from 13 TeV pp LHC collisions in 2017. Each reconstruction job was required to run on one core of a 16-core machine with Intel(R) Xeon(R) CPU @ 2.40 GHz and memory of 4 GB/core. An exponential fit (yellow dashed line) to the typical collision runs (blue scatter plot) is being compared with the performance in special high-mu run 335302 (red boxes). The high-mu run jobs were configured to produce only AOD outputs, whereas standard jobs proceeded with 12 additional output types (different DRAW, DESD, DAOD and HIST), which take extra processing time. mu_walltime_fit_combined.png
pdf-file, png-file
Digitization time per event, in HepSpec06 seconds, as a function of the average number of interactions per bunch crossing, with 25 ns bunch spacing. A linear fit to the times is overlaid. On a modern CPU, one second of wall clock time corresponds to about 10 HepSpec06 seconds. icpuVSmu_all.png
pdf-file, png-file
On this figure the total reconstruction time per event is shown for a top Monte Carlo simulation sample with 40 pileup at 13 TeV, 25 ns bunch spacing. An overall improvement of a factor 3 is visible comparing the 2012 Tier-0 release (17.2.7.9), the release 19.0.3.3 which is optimised for reconstruction of the Run-1 data and the release 19.1.1.1 which is optimised for reconstructing Run-2 data. The CPU time is shown as well separately for the Inner Detector reconstruction as the tracking is dominating the total resource needs. The simulation is done using a Run-1 detector geometry without the IBL. The HS06 scaling factor for the machine used for this study is quoted as 11.95. This is the updated comparison of the CPU time for reconstructing top pair events with Run-2 pileup in different releases, including the MC15 production release (candidate), showing a speedup factor exceeding 4. id_evtloop_cpu_time-CHEP2015.png
pdf-file, png-file
Event-wise fractional overlaps between derivations built from the muon stream, run 203875 (2012) using 5000 input events. Each cell of the plot displays the fraction of events accepted in the first format that are also accepted in the second. A higher number indicates that more events are shared between the two formats. Since the different formats contain very different numbers of events, a cell indicating the overlap of format A with format B may not have the same value as its counterpart in the other half of the square representing the overlap of B with A. Also it should be noted that these plots cover only event-wise overlaps: overlaps in variables are not displayed. Hence, it is possible that a pair of formats may be fully correlated in terms of the events selected, but may contain orthogonal sets of variables - in which case no information is shared. Finally, it can clearly be seen that overlaps vary strongly with the trigger stream producing the events. muonOverlaps-CHEP2015.png
pdf-file, png-file
Event-wise fractional overlaps between derivations built from the e-gamma stream, produced from run 203875 (2012) using 5000 input events. Each cell of the plot displays the fraction of events accepted in the first format that are also accepted in the second. A higher number indicates that more events are shared between the two formats. Since the different formats contain very different numbers of events, a cell indicating the overlap of format A with format B may not have the same value as its counterpart in the other half of the square representing the overlap of B with A. Also it should be noted that these plots cover only event-wise overlaps: overlaps in variables are not displayed. Hence, it is possible that a pair of formats may be fully correlated in terms of the events selected, but may contain orthogonal sets of variables - in which case no information is shared. Finally, it can clearly be seen that overlaps vary strongly with the trigger stream producing the events. egammaOverlaps-CHEP2015.png
pdf-file, png-file
Event-wise fractional overlaps between derivations built from the jet stream, run 203875 (2012) using 5000 input events. Each cell of the plot displays the fraction of events accepted in the first format that are also accepted in the second. A higher number indicates that more events are shared between the two formats. Since the different formats contain very different numbers of events, a cell indicating the overlap of format A with format B may not have the same value as its counterpart in the other half of the square representing the overlap of B with A. Also it should be noted that these plots cover only event-wise overlaps: overlaps in variables are not displayed. Hence, it is possible that a pair of formats may be fully correlated in terms of the events selected, but may contain orthogonal sets of variables - in which case no information is shared. Finally, it can clearly be seen that overlaps vary strongly with the trigger stream producing the events. jetsOverlaps-CHEP2015.png
pdf-file, png-file
Memory profile of ATLAS MC digitization and reconstruction jobs comparing total RSS of 8 serial jobs to RSS of one AthenaMP job with 8 worker processes. Memory savings at the reconstruction step of this particular job are ~45%. AthenaMP-vs-Serial-19.1.1.5-pileup-CHEP2015.png
pdf-file, png-file
AthenaMP schematic view Atlas-AthenaMP-Schematic-CHEP2015.png
pdf-file, png-file
Yoda scaling with number of parallel processors (cores). The plot shows how the event throughput of Atlas G4 simulation scales with number of parallel processors (cores) when running within Yoda system on the Edison HPC at NERSC (Berkeley). The scalability is already quite good, although there is certainly a room for improvement and we will be looking into it in the coming months. ATLAS-Yoda-Sim-Throughput-CHEP2015.png
pdf-file, png-file
Size of DxAOD (derivation) datasets as a fraction of the size of the parent xAOD datasets, evaluated for all derivation types across all runs in period B, for the three physics streams. Each entry in the histogram represents a single derived dataset, with the value being equal to the size of the dataset divided by the size of its parent (input) dataset. There are a total of 65 formats, three streams and more than 100 runs, leading to several thousand individual datasets. size-CHEP2015.png
pdf-file, png-file
Fraction of total input events written into the DxAOD (derivation) datasets, evaluated for all derivation types across all runs in period B, for the three physics streams. Each entry in the histogram represents a single derived dataset, with the value being equal to the number of selected events in the dataset divided by the number of input events. There are a total of 65 formats, three streams and more than 100 runs, leading to several thousand individual datasets. skim-CHEP2015.png
pdf-file, png-file
The rate of new data transformations added to the ATLAS production system. transformations.png
pdf-file, png-file
Monthly rate of task requests submitted to the ATLAS production system. tasks.png
pdf-file, png-file
Comparison of monthly rates of task requests in the ATLAS production systems ProdSys1 and ProdSys2. comparison.png
pdf-file, png-file
Comparison of the energy loss distributions for 1 GeV single muon tracks in the ATLAS Pixel and SCT Detectors for full simulation (based on the Geant4 toolkit) and FATRAS simulation. Muon_1GeV_DeltaP_2.png
pdf-file, png-file
Comparison of the energy loss eta distributions for 1 GeV single muon tracks in the ATLAS Pixel and SCT Detectors for full simulation (based on the Geant4 toolkit) and FATRAS simulation. Muon_1GeV_Eta_DeltaEProfile_3.png
pdf-file, png-file
Comparison of hit distribution of single muon tracks in the ATLAS Pixel and SCT Detectors using FATRAS tracking geometry from GeoModel and from XML configuration file. myplotRZ.png
pdf-file, png-file

/FCS_pions_layer10.png
Fig. 3: Energy fraction deposited in the 3rd layer of the Hadronic Endcap calorimeter by charged pions. The black points show the Geant4 inputs, and the result of the longitudinal energy parametrisation is shown in light blue. A good agreement is observed. The results of a Kolmogorov (KS) and chi2 test are displayed as well.

/FCS_photons_totalE.png
Fig. 4: Total cell energy deposited in the calorimeter by photons. The black points show the Geant4 inputs, and the result of the longitudinal energy parametrisation is shown in light blue. A good agreement is observed. The results of a Kolmogorov (KS) and chi2 test are displayed as well.

(a)

Closure_noWiggle.png

(b)

Closure_withWiggle.png
Fig. 4: The ratio of the FastCaloSim energy profile and the reconstructed cells energy profile, as a function of the distance of the centre of the cell and the pion calorimeter entrance position deta(pi,cell), dphi(pi,cell), for the original hit-cell assignment with the simplified geometry (a) and the modified hit-cell assignment using the wiggle hit displacement method (b). The bias in phi due to the wrong description of the accordion shape of the calorimeter in the simplified geometry is greatly reduced when using the hit displacement method.

NNeur4_Lay1_E50000_eta0.20_PID211_reference_polar.png
Fig. 5: Illustration of the energy normalized per bin area used as input to the NN fit. This example is for 50 GeV central (0.20<|eta|<0.25) pions in the EMB1 layer and corresponds to events included in the first bin of the PCA energy parameterisation.

NNeur4_Lay1_E50000_eta0.20_PID211_NNoutput_polar.png
Fig. 6: Illustration of the output of the NN parametrisation of Fig.9 input. This example is for 50 GeV central (0.20<|eta|<0.25) pions in the EMB1 layer and corresponds to events included in the first bin of the PCA energy parameterisation.

Furthermore

More material is available elsewhere concerning related activities, such as:

All ATLAS public results can be found here.


Major updates:
-- EricLancon - 2015-05-11 Responsible: MarkusElsing
Subject: public

Topic attachments
I Attachment History Action Size Date Who Comment
PDFpdf ATLAS-Yoda-Sim-Throughput-CHEP2015.pdf r1 manage 13.7 K 2015-04-01 - 12:18 EricLancon  
PNGpng ATLAS-Yoda-Sim-Throughput-CHEP2015.png r1 manage 8.4 K 2015-04-01 - 12:18 EricLancon  
PDFpdf AthenaMP-vs-Serial-19.1.1.5-pileup-CHEP2015.pdf r1 manage 17.9 K 2015-04-01 - 12:18 EricLancon  
PNGpng AthenaMP-vs-Serial-19.1.1.5-pileup-CHEP2015.png r1 manage 10.3 K 2015-04-01 - 12:18 EricLancon  
PDFpdf Atlas-AthenaMP-Schematic-CHEP2015.pdf r1 manage 62.5 K 2015-04-01 - 12:18 EricLancon  
PNGpng Atlas-AthenaMP-Schematic-CHEP2015.png r1 manage 67.4 K 2015-04-01 - 12:18 EricLancon  
PNGpng Closure_noWiggle.png r1 manage 99.1 K 2016-10-28 - 16:49 AndyHaas  
PNGpng Closure_withWiggle.png r1 manage 99.5 K 2016-10-28 - 16:49 AndyHaas  
PDFpdf FCS_photons_totalE_prelim.pdf r1 manage 17.4 K 2016-10-28 - 16:49 AndyHaas  
PNGpng FCS_photons_totalE_prelim.png r1 manage 36.5 K 2016-10-28 - 16:49 AndyHaas  
PDFpdf FCS_pions_layer10_prelim.pdf r1 manage 19.0 K 2016-10-28 - 16:49 AndyHaas  
PNGpng FCS_pions_layer10_prelim.png r1 manage 35.9 K 2016-10-28 - 16:49 AndyHaas  
PDFpdf Muon_1GeV_DeltaP_2.pdf r1 manage 28.8 K 2015-05-11 - 09:38 EricLancon  
PNGpng Muon_1GeV_DeltaP_2.png r1 manage 27.7 K 2015-05-11 - 09:38 EricLancon  
PDFpdf Muon_1GeV_Eta_DeltaEProfile_3.pdf r1 manage 24.0 K 2015-05-11 - 09:38 EricLancon  
PNGpng Muon_1GeV_Eta_DeltaEProfile_3.png r1 manage 23.2 K 2015-05-11 - 09:38 EricLancon  
PNGpng NNeur4_Lay1_E50000_eta0.20_PID211_NNoutput_polar.png r1 manage 261.8 K 2016-10-28 - 16:49 AndyHaas  
PNGpng NNeur4_Lay1_E50000_eta0.20_PID211_reference_polar.png r1 manage 267.2 K 2016-10-28 - 16:49 AndyHaas  
PDFpdf comparison.pdf r1 manage 203.9 K 2015-05-11 - 09:38 EricLancon  
PNGpng comparison.png r1 manage 226.6 K 2015-05-11 - 09:38 EricLancon  
PDFpdf cpuVSmu_all.pdf r1 manage 20.4 K 2016-02-17 - 00:17 EricLancon  
PNGpng cpuVSmu_all.png r1 manage 17.5 K 2016-02-17 - 00:17 EricLancon  
PDFpdf egammaOverlaps-CHEP2015.pdf r1 manage 167.6 K 2015-04-01 - 12:18 EricLancon  
PNGpng egammaOverlaps-CHEP2015.png r1 manage 221.1 K 2015-04-01 - 12:18 EricLancon  
PDFpdf id_evtloop_cpu_time-CHEP2015.pdf r1 manage 82.1 K 2015-04-01 - 12:18 EricLancon  
PNGpng id_evtloop_cpu_time-CHEP2015.png r1 manage 140.3 K 2015-04-01 - 12:18 EricLancon  
PDFpdf id_evtloop_cpu_time.pdf r1 manage 14.0 K 2014-08-01 - 16:50 MarkusElsing  
PNGpng id_evtloop_cpu_time.png r1 manage 139.2 K 2014-08-01 - 16:50 MarkusElsing  
PDFpdf jetsOverlaps-CHEP2015.pdf r1 manage 183.3 K 2015-04-01 - 12:18 EricLancon  
PNGpng jetsOverlaps-CHEP2015.png r1 manage 240.2 K 2015-04-01 - 12:19 EricLancon  
PDFpdf muonOverlaps-CHEP2015.pdf r1 manage 160.3 K 2015-04-01 - 12:18 EricLancon  
PNGpng muonOverlaps-CHEP2015.png r1 manage 207.4 K 2015-04-01 - 12:19 EricLancon  
PDFpdf myplotRZ.pdf r1 manage 25.1 K 2015-05-11 - 09:39 EricLancon  
PNGpng myplotRZ.png r1 manage 17.7 K 2015-05-11 - 09:39 EricLancon  
PDFpdf size-CHEP2015.pdf r1 manage 55.9 K 2015-04-01 - 12:18 EricLancon  
PNGpng size-CHEP2015.png r1 manage 60.8 K 2015-04-01 - 12:18 EricLancon  
PDFpdf skim-CHEP2015.pdf r1 manage 49.6 K 2015-04-01 - 12:18 EricLancon  
PNGpng skim-CHEP2015.png r1 manage 52.8 K 2015-04-01 - 12:18 EricLancon  
PDFpdf tasks-CHEP2015.pdf r1 manage 106.5 K 2015-04-01 - 12:18 EricLancon  
PNGpng tasks-CHEP2015.png r1 manage 128.4 K 2015-04-01 - 12:18 EricLancon  
PDFpdf tasks.pdf r1 manage 106.5 K 2015-05-11 - 09:39 EricLancon  
PNGpng tasks.png r1 manage 128.4 K 2015-05-11 - 09:39 EricLancon  
PDFpdf transformations.pdf r1 manage 51.3 K 2015-05-11 - 09:39 EricLancon  
PNGpng transformations.png r1 manage 61.8 K 2015-05-11 - 09:39 EricLancon  
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