December 5, 2012 MASTODONS meeting Laboratoire de lAccélérateur Linéaire (LAL) / CNRS & Université Paris Sud Laboratoire de la Recherche en Informatique

December 5, 2012

MASTODONS meeting

Laboratoire de l’Accélérateur Linéaire (LAL) / CNRS & Université Paris SudLaboratoire de la Recherche en Informatique (LRI) / CNRS & INRIA & Université Paris SudLaboratoire de l’Informatique du Parallélisme (LIP) / CNRS & INRIA & ENS Lyon & UCB Lyon

PI: Balázs Kégl (LAL) presented by Cécile Germain (LRI)

Data in physics: Large-scale data storage, data management, and data analysis for next generation particle physics experiments

DeePhy

Balázs is at NIPS

Cécile Germain / LRI MASTODONS DeePhy

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The collaboration

LAL

AppStat

Auger

TAOLRI

CompSci / Statistics

Experimental Physics

ATLAS

ILC/Calice

LIP

Engineering support

Service Informatique

AVALON


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The participants

•Laboratoire de l’Accélérateur Linéaire (LAL)

•Rémi Bardenet (PhD student, AppStat), Djalel Benbouzid (PhD student, AppStat), François-David Collin (IR CNRS, SI), Laurent Duflot (CR CNRS, ATLAS), Diego Garcia Gamez (postdoc, Auger), Michel Jouvin (IR CNRS, SI), Balázs Kégl (CR CNRS, AppStat&Auger), Oleg Lodygensky (IR CNRS, SI), Roman Poeschl (ILC, DR CNRS), David Rousseau (DR CNRS, ATLAS)

•Laboratoire de la Recherche en Informatique (LRI)

•Cécile Germain (Professeur, UPSud, TAO), Tristan Glatard (CR CNRS, Laboratoire CREATIS), Michèle Sebag (DR CNRS, TAO)

•Laboratoire de l’Informatique du Parallélisme (LIP)

•Simon Delamare (IR CNRS), Gilles Fedak (CR INRIA), Laurent Lefêvre (CR INRIA)


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Projects

•ANR Siminole: 2010-2014, LAL/LRI/Telecom ParisTech

•Large-scale simulation-based probabilistic inference, optimization, and discriminative learning with applications in experimental physics

•ANR MapReduce: ??

•MRM Grille Paris Sud: 2010-2014, LAL/LRI

•FP7 EDGI/DEGISCO: ??

•...


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Meetings

•Regular (phone) meetings between the PIs

•Project meeting, November 23

•Clouds pour le Calcul Scientifique: November 27-28, 2012 LAL@Orsay

•The First International Workshop on BigData in Science: Systems, Infrastructures and Applications (BIGDATA'2013): October 3-4, 2013 ICPP, Lyon, FRANCE


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Budget 2012

• LAL

• LRI

• LIP


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Mission of AppStat@LAL

•Motivate fundamental research by real applications

•Bring state-of-the-art analysis techniques to experimental physics

Computational Statistics

High-energy physics

Data analysis methodology

Real dataMotivation

Where we are in 2012


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Triggers and lean classifiers

D. Benbouzid, R. Busa-Fekete, and B. Kégl, “Fast classification using sparse decision DAGs,”, International Conference on Machine Learning (ICML), 2012

The telescope image The JEM EUSO telescope on the ISS

Trigger = fast classifier: signal vs. background

Boosting works well but produces slow classifiersWe designed a Markov decision graph (MDDAG) algorithm using reinforcement learning

MDDAG can be reused in any test-time constrained problem (object detection, web page ranking), can replace classical cascade designs

Leads to interesting research questions (sparsity, representation, deep learning)



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Adaptive Metropolis for mixture signals

R. Bardenet, O. Cappé; , G. Fort, and B. Kégl, “Adaptive Metropolis with online relabeling,” in International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.

The Auger tank signalCosmic rays and the Pierre Auger observatory

Classical adaptive Metropolis was suboptimal due to symmetries (label switching)

We designed adaptive Metropolis with online relabeling (AMOR) that works well on our problem

AMOR is extendible to any problem involving inference on parametrized mixture signals



L’équipe-projet TAO de l’INRIA-Saclay

5 chercheurs ou EC seniors

9 juniors

25 PhD, post-docs et Ingénieurs

Computer-Go : 3 Gold medals, 2010

CMAES : de la théorie de l’optimisation stochatstique aux applications avec EADS, IFP, PSA, SIMINOLE

Autonomics + e-Science: Grid Observatory, Green Computing Observatory avec EGI


Digital curation of the behavioural data of the EGI grid: observe and publishwww.grid-observatory.orgSpecific challenges for analysis “unusual” extreme statistics: which metrics? Are our systems stationary? (in fact no)Optimisation, autonomics How to build the knowledge? No Gold

Standard, too rare experts Infer latent causes, eg analyze traces as text files, and more Build credible benchmarks and models, eg piecewise AR instead of long range dependence

The Grid Observatory

Managed ElementES

Monitor

Analyze

Execute

Plan

Knowledge

Autonomic ManagerES

Cécile Germain / LRI MASTODONS DeePhyFailure: it’s not a bug, it’s a feature[D. Feng, C.Germain and T. Glatard. Distributed Monitoring with Collaborative Prediction.

In « 12th IEEE International Symposium on Cluster, Cloud and Grid Computing (CCGrid'12) » 2012]

« A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable »

BDII

LFC

SE

VOMS

CE

N2

N3N1

N4

firewallService/hardware

N5

CEn.…

k.…

n.…

SE SE SE…..

……

ce-hd

bdii lfc voms ….

lcg-cr 1 1 1 1

nmap 1 1 0 0

srm-ls 1 0 1 1

….Dependency

matrix

Collaborative prediction more efficient than detection/diagnosis

Cécile Germain / LRI MASTODONS DeePhyFailure: it’s not a bug, it’s a feature[D. Feng, C.Germain and T. Glatard. Distributed Monitoring with Collaborative Prediction.

In « 12th IEEE International Symposium on Cluster, Cloud and Grid Computing (CCGrid'12) » 2012]

« A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable »

BDII

LFC

SE

VOMS

CE

N2

N3N1

N4

firewallService/hardware

N5

CEn.…

k.…

n.…

SE SE SE…..

……

Probe matrix

Collaborative prediction more efficient than detection/diagnosis

lcg-cp


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BigData and BigComputationScientific challenges for the next 4-5 years

The LHC The EGEE/EGI gridThe ATLAS collaboration

largest: 26 kmhighest energy: 7 TeVcoldest: 1.9 Kemptiest: (check because13 atm is very high pressure)

largest: 200K CPUs, 15 PBmost distributed: 250 sitesbusiest: 1000 jobs/day

largest: 3000 scientistswidest: 38 countries and 174 institutions


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•Opportunities

•We can apply computationally expensive analysis techniques we could not dream of ten years ago

•The Google cat: deep learning technique running on 16K cores for three days, watching 10M YouTube stills[Le et al., ICML’12]

•Large-scale parallel Monte-Carlo Markov chains or likelihood-free simulation based approximate Bayesian computation [Tavaré et al., 1997]


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•Challenges

•Large-scale machine learning is a new paradigm:

•statistical overfitting is no longer a danger, underfitting is

•optimization time interferes with the approximation-estimation trade-off, mediocre optimization algorithms like stochastic gradient descent become competitive. [Bottou-Bousquet, 2008, 2011]

•The algorithmic toolbox shrinks considerably: it is often better to use a large data set and a suboptimal but fast algorithm then a small data set and an optimal expensive technique

•Data management and work-flow become crucial, often more important than optimizing the single-thread technique


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Data as a communication tool between Physics and CS

•A well-designed challenge can attract a large number of professional data miners

•Tricky:

•data cleaning and formatting, defining a standardized problem, evaluation metric, scripting, web interface, marketing

•requires the collaboration of physicists, computer scientists, and engineers

•The prize is worthy: objective evaluation of a large number of techniques on a given problem

What we will do in 2013


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The Higgs boson challenge

Observation à plus de 5 sigmad’une particule dont les propriétéssont compatible avec celle du bosonde Higgs Phys. Lett. B 716 (2012) 1-29

Spectre filtré à partir de quelquesmilliards de collisions proton-proton enregistrées

Piètre resolutionPic du signal à coté d’un pic de bruit de fond large et important

Il s’agit maintenant d’établir que la nouvelle particule est vraiment le boson de Higgs qu’on attend en la détectant dans d’autres canaux plus difficiles


Atlas

Signal Higgs

http://www.sciencedirect.com/science/article/pii/S037026931200857X


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•Challenge 1: estimating the mass of the Higgs boson candidate

•as precisely as possible (at the moment: 20%)

•within a fixed CPU time (0.1s par event)

•Monte-Carlo integration in 5-6 dimensions with constraints

•Adaptive importance sampling, adaptive MCMC, etc.



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•Challenge 2: detecting the Higgs boson

•standard classification problem with ~100 features at two classes: signal vs. background

•features are constructed “manually”

•AdaBoost, SVM, neural networks, etc.

•Challenge 3: unsupervised feature extraction

•two-class classification on “raw” input: a variablesize list of particles

•targeting the deep learning community


• Challenges scientifiques du projet à 4 ou 5 ans et affinage éventuel de ces challenges depuis la soumission des projets,

• Organisation du projet, modalités de travail collaboratif, agenda des réunions techniques de l'année 2012,

• Premiers résultats scientifiques obtenus ou le cas échéant identification des premières pistes de recherche explorées et positionnement par rapport à l'état de l'art,

• Objectifs scientifiques pour 2013, en donner une vue plus détaillée que celle du point 1.

• 1 slide sur l'utilisation du budget alloué au projet.

Documents

December 5, 2012 MASTODONS meeting Laboratoire de lAccélérateur Linéaire (LAL) / CNRS & Université Paris Sud Laboratoire de la Recherche en Informatique