contributions/virgo/AdV_computing_CNAF.tex

@@ -5,18 +5,18 @@
 
 %\author{P. Astone$^1$, F. Badaracco$^{2,3}$, S. Bagnasco$^4$, S. Caudill$^5$, F. Carbognani$^6$, A. Cirone$^{7,8}$, G. Fronz\'e$^{4}$, J. Harms$^{2,3}$, I. LaRosa$^1$, C. Lazzaro$^9$, P. Leaci$^1$, S. Lusso$^4$, C. Palomba$^1$, R. DePietri$^{11,12}$, M. Punturo$^{10}$, L. Rei$^8$, L. Salconi$^6$, S. Vallero$^{4}$, on behalf of the Virgo collaboration}
 \author{P. Astone$^1$, F. Badaracco$^{2,3}$, S. Bagnasco$^4$, S. Caudill$^5$, F. Carbognani$^6$, A. Cirone$^{7,8}$, M. Drago$^{2,3}$, G. Fronz\'e$^{4}$, J. Harms$^{2,3}$, I. LaRosa$^1$, C. Lazzaro$^9$, P. Leaci$^1$, S. Lusso$^4$, C. Palomba$^1$, R. DePietri$^{11,12}$, M. Punturo$^{10}$, L. Rei$^8$, L. Salconi$^6$, S. Vallero$^{4}$, on behalf of the Virgo collaboration}
-\address{$^1$ INFN, Roma, IT}
-\address{$^2$ Gran Sasso Science Institute (GSSI), IT}
-\address{$^3$ INFN, Laboratori Nazionali del Gran Sasso, IT}
-\address{$^4$ INFN, Torino, IT}
-\address{$^5$ Nikhef, Science Park, NL}
-\address{$^6$ EGO-European Gravitational Observatory, Cascina, Pisa, IT}
-\address{$^7$ Universit\`a degli Studi di Genova, IT}
-\address{$^8$ INFN, Genova, IT}
-\address{$^9$ INFN, Padova, IT}
-\address{$^{10}$ INFN, Perugia, IT}
-\address{$^{11}$ Universit\`a degli Studi di Parma, IT}
-\address{$^{12}$ INFN, Gruppo Collegato Parma, IT}
+\address{$^1$ INFN Sezione di Roma, Roma, IT}
+\address{$^2$ Gran Sasso Science Institute (GSSI), L'Aquila, IT}
+\address{$^3$ INFN Laboratori Nazionali del Gran Sasso, L'Aquila, IT}
+\address{$^4$ INFN Sezione di Torino, Torino, IT}
+\address{$^5$ Nikhef, Amsterdam, NL}
+\address{$^6$ EGO-European Gravitational Observatory, Cascina (PI), IT}
+\address{$^7$ Universit\`a degli Studi di Genova, Genova, IT}
+\address{$^8$ INFN Sezione di Genova, Genova, IT}
+\address{$^9$ INFN Sezione di Padova, Padova, IT}
+\address{$^{10}$ INFN Sezione di Perugia, Perugia, IT}
+\address{$^{11}$ Universit\`a degli Studi di Parma, Parma, IT}
+\address{$^{12}$ INFN Gruppo Collegato Parma, Parma, IT}
 
 %\address{Production Editor, \jpcs, \iopp, Dirac House, Temple Back, Bristol BS1~6BE, UK}
 
@@ -32,7 +32,7 @@ The amount of data processed during the last few years has emphasized the fact t
 
 \section{Advanced Virgo computing model}
 \subsection{Data production and data transfer}
-The Advanced Virgo data acquisition system is writing about 35MB/s of data (so-called ``bulk data'') during O3. CNAF and CC-IN2P3 are the Virgo Tier-0: during the science runs, bulk data is stored in a circular buffer located at the Virgo site, and simultaneously transferred to the remote computing centres where they are archived in tape libraries. The transfer is realized through an ad-hoc procedure based on GridFTP (at CNAF) and iRods (at CC-IN2P3). Other data fluxes reach CNAF during science runs:
+The Advanced Virgo data acquisition system is writing about 35MB/s of data (so-called ``bulk data'') during O3. CNAF and CC-IN2P3 are the Virgo Tier 0: during the science runs, bulk data is stored in a circular buffer located at the Virgo site, and simultaneously transferred to the remote computing centers where they are archived in tape libraries. The transfer is realized through an ad-hoc procedure based on GridFTP (at CNAF) and iRods (at CC-IN2P3). Other data fluxes reach CNAF during science runs:
 
 \begin{itemize}
 	\item trend data (few GB/day), periodically transferred using the system described above;
@@ -42,23 +42,23 @@ The Advanced Virgo data acquisition system is writing about 35MB/s of data (so-c
 
 \subsection{Data Analysis at CNAF}
 %The analysis of the LIGO and Virgo data was made jointly by the two collaborations; the analysis pipelines are distributed among the worldwide network of computing facilities offering computing resources to the GW experiments. CNAF was mainly used for CW analysis, looking for continuous gravitational wave signals, developed by INFN–Roma people (see hereafter more details). But at CNAF is also running part of the pyCBC pipeline, submitted via OSG, looking for compact binaries signals. pyCBC has a crucial role in the detection of the coalescence of  BBH and BNS. CNAF contributed to the computation performed through pyCBC for the analysis of the events GW170814, the first BBH coalescence detected also by Virgo, and GW170817, the BNS coalescence. During the last month a new extension of CVMFS, \emph{big cvmfs} was mounted at cnaf to support another OSG pipeline, \emph{BayesWave}. The big cvmfs is able to export, in a posix fashion, big file of data from nearby cache in Amsterdam instead of accessing data directly from Nebraska. BayesWave is a Bayesian algorithm designed to robustly distinguish gravitational wave signals from noise and instrumental glitches without relying on any prior assumptions of waveform morphology. In the last year coherent WaveBurst \emph{cwb} was ported to cnaf and made available to run. cwb is a pipeline based on coherent algorithm for detection and reconstruction of modelled and unmodelled GW bursts. A new newtonian noise cancellation algoritmh, developed by the group of Gran Sasso Science Institute (\emph{GSSI}) was made available very recently. The increased number of LVC pipelines running at cnaf has led to saturate advance virgo pledge at cnaf, cnaf promptly rensponded to advance virgo needed enlargin our quota and giving experimental access to gpu. 
-LIGO-Virgo data analysis is organized jointly, meaning that the analysis pipelines are made available to the computing facilities related to the LVC network, ready to be distributed to each GW detector. CNAF has been mainly used for Continuous Wave(\emph{CW}) analysis, led by the Roma INFN group, and for the Compact Binary Coalescence python-based analysis (\emph{pyCBC}), submitted via OSG. In particular CNAF computationally contributed to GW170814 and GW170817 events, respectively the first BBH coalescence detected by Virgo and the first BNS merger ever observed. During the last month a new extension of CVMFS, so-called ``big cvmfs'', was mounted at CNAF to support another OSG-based pipeline, Bayes Wave. The former is able to make available, in a POSIX-like fashion, big data files from a cache in Amsterdam, instead of accessing the data directly from Nebraska. The latter is a Bayesian algorithm, designed to robustly distinguish GW signals from noise and instrumental glitches, without relying on any prior assumptions on the waveform shape. During the last year, coherent WaveBurst(\emph{cWB}), an algorithm dedicated to the detection and reconstruction of GW Bursts, was also ported to CNAF. Furthermore, new Newtonian Noise cancellation algorithms, which are currently being developed by the GSSI group, were made recently available. The increasing number of LVC pipelines running at CNAF has led to resource saturation, and consequently to a demand for enlarged  computing power, together with access to GPUs.
+LIGO-Virgo data analysis is organized jointly, meaning that the analysis pipelines are made available to the computing facilities related to the LVC network, ready to be distributed to each GW detector. CNAF has been mainly used for Continuous Wave (\emph{CW}) analysis, led by the Roma INFN group, and for the Compact Binary Coalescence python-based analysis (\emph{pyCBC}), submitted via OSG. In particular CNAF computationally contributed to GW170814 and GW170817 events, respectively the first BBH coalescence detected by Virgo and the first BNS merger ever observed. During the last month a new extension of CVMFS, so-called ``big cvmfs'', was mounted at CNAF to support another OSG-based pipeline, Bayes Wave. The former is able to make available, in a POSIX-like fashion, big data files from a cache in Amsterdam, instead of accessing the data directly from Nebraska. The latter is a Bayesian algorithm, designed to robustly distinguish GW signals from noise and instrumental glitches, without relying on any prior assumptions on the waveform shape. During the last year, coherent WaveBurst (\emph{cWB}), an algorithm dedicated to the detection and reconstruction of GW Bursts, was also ported to CNAF. Furthermore, new Newtonian Noise cancellation algorithms, which are currently being developed by the GSSI group, were made recently available. The increasing number of LVC pipelines running at CNAF has led to resource saturation, and consequently to a demand for enlarged  computing power, together with access to GPUs.
 
 \subsubsection{CW pipeline}
 
-CNAF has been in 2018 the main computing center for Virgo all-sky continuous wave (CW) searches. The search for this kind of signals, emitted by spinning neutron stars, covers a large portion of the source parameter space and consists of several steps organized in a hierarchical analysis pipeline. CNAF has been mainly used for the ``incoherent'' stage, based of a particular implementation of the Hough transform, which is the heaviest part of the analysis from a computational point of view. The code implementing the Hough transform has been written in such a way that the exploration of the parameter space can be split in several independent jobs, each covering a range of signal frequencies and a portion of the sky. This is an embarrassingly parallel problem, very well suited to be run in a distributed computing environment. The analysis jobs have been run using the EGI UMD grid middleware, with input and output files stored in a StoRM-based Storage Element at CNAF. Candidate post-processing, consisting of clusterisation, coincidences and ranking, and parts of the candidate follow-up analysis have been also carried on at CNAF. Typical Hough transform jobs needs about 4GB of memory (with a fraction requiring more, up to 8GB). Past year most of the resources have been used to analyze Advanced LIGO O2 data. Overall, in 2018 more than 10M CPU hours have been used at CNAF for CW searches, by running O($10^5$) jobs, with duration from a few hours to ~3 days.
+CNAF has been in 2018 the main computing center for Virgo all-sky continuous wave (CW) searches. The search for this kind of signals, emitted by spinning neutron stars, covers a large portion of the source parameter space and consists of several steps organized in a hierarchical analysis pipeline. CNAF has been mainly used for the ``incoherent'' stage, based of a particular implementation of the Hough transform, which is the heaviest part of the analysis from a computational point of view. The code implementing the Hough transform has been written in such a way that the exploration of the parameter space can be split in several independent jobs, each covering a range of signal frequencies and a portion of the sky. This is an embarrassingly parallel problem, very well suited to be run in a distributed computing environment. The analysis jobs have been run using the EGI UMD grid middleware, with input and output files stored in a StoRM-based Storage Element at CNAF. Candidate post-processing, consisting of clusterisation, coincidences and ranking, and parts of the candidate follow-up analysis have been also carried on at CNAF. A typical Hough transform job needs about 4GB of memory (with a fraction requiring more, up to 8GB). Past year most of the resources have been used to analyze Advanced LIGO O2 data. Overall, in 2018 more than 10M CPU hours have been used at CNAF for CW searches, by running O($10^5$) jobs, with duration from a few hours to ~3 days.
 
 \subsubsection{cWB pipeline}
-Starting in 2019, the coherent WaveBurst based pipelines have been ported and adapted to run at CNAF to reproduce the cWB environment setup on the worker nodes, without the constraint to read the user home account during running. It is planned to run at CNAF all Virgo offline long duration all-sky searches on the data that will be collected during the Observational Run 3 (03) that started April 1st, 2019.  cWB is a data-analysis tool to search for a broad range of gravitational-wave (GW) transients. The pipeline identifies coincident events in the GW data from earth-based interferometric detectors and reconstructs the gravitational wave signal by using a constrained maximum likelihood approach. The algorithm performs a time-frequency analysis of the data, using wavelet representation, and identifies the events by clustering time-frequency pixels with significant excess coherent power. The likelihood statistics is built as a coherent sum over the responses of different detectors and estimates the total signal to noise ratio of the GW signal in the network. The pipeline splits the total analysis time into sub-periods to be analyzed in parallel jobs, using HTCondor tools and it is expected to use a consistent amount of CPU hours during 2019.
+Starting in 2019, the coherent WaveBurst based pipelines have been ported and adapted to run at CNAF to reproduce the cWB environment setup on the worker nodes, without the constraint to read the user home account during running. It is planned to run at CNAF all Virgo offline long duration all-sky searches on the data that will be collected during the Observational Run 3 (03) that started April 1, 2019.  cWB is a data-analysis tool to search for a broad range of gravitational-wave (GW) transients. The pipeline identifies coincident events in the GW data from earth-based interferometric detectors and reconstructs the gravitational wave signal by using a constrained maximum likelihood approach. The algorithm performs a time-frequency analysis of the data, using wavelet representation, and identifies the events by clustering time-frequency pixels with significant excess coherent power. The likelihood statistics is built as a coherent sum over the responses of different detectors and estimates the total signal to noise ratio of the GW signal in the network. The pipeline splits the total analysis time into sub-periods to be analyzed in parallel jobs, using HTCondor tools and it is expected to use a consistent amount of CPU hours during 2019.
 
 \subsubsection{Newtonian noise pipeline}
 The cancellation of gravitational noise from seismic fields will be a major challenge both from theoretical and computational point of view, since the involved simulations  are very demanding. This activity requires the accurate positioning of a large number of seismometers. A cluster at CNAF was used to run position optimisations of the seismic arrays used for cancellation and to determine the cancellation performance as a function of the number of sensors and its robustness with respect to sensor-positioning accuracy.
 
-\subsection{outlook}
-The first detection of gravitational waves (GW) and the birth of multi-messenger astrophysics have opened a new field of scientific research. With the possibility to detect GW from various kind of sources we can probe new physical phenomena in regions of the Universe we couldn't explore before, with new perspectives on our knowledge about how it works. 
-Indeed, so far only signals from the coalescence of compact objects have been detected, while one of the most interesting and promising class of continuous GW signals, coming from asymmetrical rotating neutron stars, is still missing. Wide searches of this kind of signals require a huge amount of computational power due to the Doppler effect of the Earth motion, which disrupts the incoming signal dramatically increases the parameters space. This means that it is necessary to develop complex algorithms to reduce the computational power needed, at the price of significantly reducing the sensitivity of the search.  
+\subsection{Outlook}
+The first detection of gravitational waves (GW) and the birth of multi-messenger astrophysics have opened a new field of scientific research. With the possibility to detect GW from various kinds of sources we can probe new physical phenomena in regions of the Universe we couldn't explore before, with new perspectives on our knowledge about how it works. 
+Indeed, so far only signals from the coalescence of compact objects have been detected, while one of the most interesting and promising class of continuous GW signals, coming from asymmetrical rotating neutron stars, is still missing. Wide searches of this kind of signals require a huge amount of computational power due to the Doppler effect of the Earth motion, which disrupts the incoming signal and dramatically increases the parameters space. This means that it is necessary to develop complex algorithms to reduce the computational power needed, at the price of significantly reducing the sensitivity of the search.  
 The development of new algorithms, which use the high efficiency and computational power of modern GPUs, showed that the new codes on a single GPU can run with a factor of ten speed-up with respect to the older ones on a ten times more expensive multi-core CPU. 
-For the CW case, using real data from the 9 months long run of the LIGO detectors we have estimated that on a cluster of about 200 GPUs a complete search can be done in about a couple of months, to be confronted with the several months required by the older code on a 2000 CPUs cluster.\\ A GPU cluster would be also extremely useful to test and train  Machine Learning algorithms, which in the recent years were shown to be able to face very complex analyses with high efficiency and speed.\\
+For the CW case, using real data from the 9 months long run of the LIGO detectors we have estimated that on a cluster of about 200 GPUs a complete search can be done in about a couple of months, to be compared with the several months required by the older code on a 2000 CPUs cluster.\\ A GPU cluster would be also extremely useful to test and train  Machine Learning algorithms, which in the recent years were shown to be able to face very complex analyses with high efficiency and speed.\\
 Advanced Virgo and Advanced LIGO are also exploring different technologies to face the new challenges of GW physics. The growing number of computing centers involved in GW research forces us to relax our idea on computing, searching a way to uniformly run different pipelines in complex and heterogeneous infrastructures. For example, the de-supporting of GridFTP pushes towards the use of Rucio, a well supported and flexible tool for data-transfer and management, while the de-supporting of the Cream-CE suggests a redesign of the job submission strategy, possibly under the control of an overall management system like DIRAC. \\ CNAF staff is intensively supporting Virgo members in all this these tests. 
 
 

contributions/xenon/main.tex

@@ -3,6 +3,7 @@
 \usepackage[american]{babel}
 \usepackage{geometry}
 %\usepackage{fancyhdr}
+\usepackage{graphicx}
 
 \geometry{a4paper,top=4.0cm,left=2.5cm,right=2.5cm,bottom=2.7cm}
 
@@ -19,9 +20,9 @@
 
 \title{XENON computing model}
 %\pagestyle{fancy}
-\author{M. Selvi}
+\author{Marco Selvi$^1$}
 
-\address{INFN - Sezione di Bologna}
+\address{$^1$ INFN Sezione di Bologna, Bologna, IT}
 
 \ead{marco.selvi@bo.infn.it}
 
@@ -46,7 +47,7 @@ dark matter. These indirect evidences of its existence
 triggered a world-wide effort to try observe its interaction with
 ordinary matter in extremely sensitive detectors, but its nature is
 still a mystery.
-The XENON experimental program \cite{225, mc, instr-1T, sr1} is searching
+The XENON experimental program \cite{225, mc, instr-1T} is searching
 for weakly interacting massive particles (WIMPs), hypothetical
 particles that, if existing, could account for dark matter and
 that might interact with ordinary matter through nuclear recoil.
@@ -102,20 +103,20 @@ In fig. \ref{fig:xenonCM} we show a sketch of the XENON computing model and data
 
 \begin{figure}[t]
 \begin{center}
-\includegraphics{xenon-computing-model.pdf}
+\includegraphics[width=15cm]{xenon-computing-model.pdf}
 \end{center}
 \caption{Overview of the XENON1T Job and Data Management Scheme.}
 \label{fig:xenonCM}
 \end{figure}
 
 The resources at CNAF (CPU and Disk) are used so far mainly for the Monte Carlo simulation of the
-detector (GEANT4 model of the detector and waveform generator), and for the €œreal-data€ storage and processing.  Currently we used about 12 TB of the 200 TB available for 2018. 
+detector (GEANT4 model of the detector and waveform generator), and for the €œreal-data€ storage and processing.  %Currently we used about XX TB of the XX TB available for 2018. %Help
 %For this purpose, 
 There were some improvements performed recently by the Computing Working group of the experiment. The CNAF Disk at the beginning was not integrated into the Rucio framework because it was not large enough to justify the amount of work needed for the integration (it was 60 TB up to 2016). For this reason we required for 2018 an additional amount of 90 TB, to reach a total 200 TB which is considered significant by the collaboration to consider a full integration of the Disk space.\\
 The second improvement has been to perform the data processing on both the US and EU GRID (previously it was done in the US only). Some software tools have been successfully developed and tested during 2017, and they are used for a fully distributed massive data processing. To fulfil this goal, we required 300 HS06 additional CPUs, for a total of 1000 HS06, equivalent to the resources available on the US OSG.\\
 The request of Tapes (1000 TB) in 2018 was done to fulfil the requirement by INFN to have a copy of all the XENON1T data in Italy,  as discussed inside the INFN Astroparticle Committee. A dedicate automatic data transfer to tapes has been developed by CNAF.
 
-The computing model described in this report allowed for a fast and effective analysis of the first XENON1T data in 2017, and the final ones in 2018, which lead to the best limit in the search of WIMPs so far \cite{sr0, sr1}.
+The computing model described in this report allowed for a fast and effective processing and analysis of the first XENON1T data in 2017, and of the final ones in 2018, which led to the best limit in the search of WIMPs so far \cite{sr0, sr1}.
 
 \section{XENONnT}
 The planning and initial implementation of the data and job management
@@ -142,19 +143,19 @@ volume of XENON1T.
 
 \begin{thebibliography}{9}
 
-\bibitem{225} Aprile E. et al (XENON Collaboration), {\it Dark Matter Results from 225 Live Days of XENON100 Data},\\ 2012, Phys. Rev. Lett. {\bf 109}, 181301 
+\bibitem{225} Aprile E. et al (XENON Collaboration), {\it Dark Matter Results from 225 Live Days of XENON100 Data}, Phys. Rev. Lett. {\bf 109} (2012), 181301 
 
-\bibitem{mc} Aprile E. et al (XENON Collaboration), {\it Physics reach of the XENON1T dark matter experiment},\\ 2016, JCAP {\bf 04}, 027
+\bibitem{mc} Aprile E. et al (XENON Collaboration), {\it Physics reach of the XENON1T dark matter experiment}, JCAP {\bf 04} (2016), 027
 
-\bibitem{instr-1T} Aprile E. et al (XENON Collaboration), {\it The XENON1T Dark Matter Experiment},\\ Eur. Phys. J. C77  {\bf 12}, 881 (2017)
-
-\bibitem{sr1} Aprile E. et al (XENON Collaboration), {\it Dark Matter Search Results from a One Ton-Year Exposure of XENON1T},\\ 2018, Phys. Rev. Lett. {\bf 121}, 111302 
+\bibitem{instr-1T} Aprile E. et al (XENON Collaboration), {\it The XENON1T Dark Matter Experiment}, Eur. Phys. J. C77  {\bf 12} (2017), 881 
 
 \bibitem{osg} Ruth Pordes et al., {\it The open science grid}, Journal of Physics: Conference Series 78, 1 (2007), 012057. 
 
 \bibitem{egi}  D. Kranzlmüller et al., {\it The European Grid Initiative (EGI)}, Remote Instrumentation and Virtual Laboratories. Springer US, Boston, MA, 61–66 (2010).
 
-\bibitem{sr0} Aprile E. et al (XENON Collaboration), {\it First Dark Matter Search Results from the XENON1T Experiment },\\ 2017, Phys. Rev. Lett. {\bf 119}, 181301 
+\bibitem{sr0} Aprile E. et al (XENON Collaboration), {\it First Dark Matter Search Results from the XENON1T Experiment }, Phys. Rev. Lett. {\bf 119} (2017), 181301 
+
+\bibitem{sr1} Aprile E. et al (XENON Collaboration), {\it Dark Matter Search Results from a One Ton-Year Exposure of XENON1T}, Phys. Rev. Lett. {\bf 121} (2018), 111302 
   
   
 \end{thebibliography}

immagini/convert_to_pdf.sh

+#!/bin/bash
+for file in `ls -l | grep -v total | awk '{print $9}'`; do sudo convert $file ${file::(-4)}.pdf; done