Optimizing Neural Network Classifiers with ROOT on a Rocks Linux Cluster Tomas Lind´en, Francisco Garc´ıa, Aatos Heikkinen, and Sami Lehti Helsinki Institute of Physics, University of Helsinki, POB 64, FIN-00014, Finland,
[email protected], WWW home page: http://www.physics.helsinki.fi/~tlinden/
Abstract. We present a study to optimize multi-layer perceptron (MLP) classification power with a Rocks Linux cluster [1]. Simulated data from a future high energy physics experiment at the Large Hadron Collider (LHC) is used to teach a neural network to separate the Higgs particle signal from a dominant background [2]. The MLP classifiers have been implemented using the ROOT data analysis framework [3]. We utililize features of the Parallel ROOT facility (PROOF) [4] to analyze our data and to understand the functionality of the neural networks. PROOF is designed for interactive parallel data analysis of large data sets. Our aim is to reach a stable physics signal recognition for new physics and a well understood background rejection. We report on the performance of PROOF and on the integration of PROOF with the cluster environment in use and on the physics performance of new neural classifiers developed in this study.
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Introduction
A neural network (NN) approach has been shown to be applicable to the problem of Higgs boson detection at the Large Hadron Collider. We study the use of NNs ¯ SUSY , HSUSY → τ τ in the Compact in the problem of tagging b jets in pp→, bbH Muon Solenoid experiment. B tagging is an important tool for separating the Higgs events with associated b jets from the Drell-Yan background Z, γ ∗ → τ τ , for which the associated jets are mostly light quark and gluon jets.
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Computational environment
NPACI Rocks Cluster Distribution is a cluster management software for scientific computation based on Red Hat Linux, supporting cluster installation, configuration, monitoring and maintenance [5]. Versions 3.2. and 3.3 used in this work are based on Red Hat Enterprise Linux 3.0. Several Rocks based clusters have made it to the Top500 list [6], for a current list of the installed Rocks clusters, see the Rocks website [7]. There are two Rocks production clusters available at our institute. The larger one is a 64-bit 1.8/2.2 GHz AMD Opteron cluster called ametisti, which has
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Fig. 1. Successfully reconstructed jets can be identified as b jets using a lifetime based tagging algorithm, which relies on displaced secondary vertices’s.
132 CPUs in 66 compute nodes with 2/4 GB RAM. On ametisti there is one dedicated Gb/s network for communication and another dedicated Gb/s network for NFS-traffic to enhance the performance of the shared NFS disk system. In addition to this there is also a fast ethernet network used for remote management. The smaller one is a 32-bit 2.13 GHz AMD Athlon cluster called mill, which has 64 CPUs in 32 compute nodes with 1 GB RAM connected with fast ethernet network to the Gb/s network interface of the cluster frontend.
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Neural networks in the ROOT data analysis framework
ROOT provides a flexible object oriented implementation of multi-layer perceptrons (MLPs). Various learning methods such as Steepest descent algorithm, Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS) and variants of conjugate gradients are provided with visualization of the network architecture and learning process. The networks can be exported as a standalone C++ code. In our neural network approach to the b tagging problem, we feed the networks with the same events (see Fig. 1) as used in the traditional methods [8, 9], including information on the number of tracks in the jet cone, leading track impact parameters (for a detailed description see [8]) and impact parameter significances. Finding an optimal set of variables for teaching an optimal MLP configuration (see Fig. 2) is a multidimensional, computationally demanding optimization task suitable for solving with a cluster equipped with ROOT and PROOF.
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Fig. 2. Visualization of neuron weights of a multi-layer perceptron 7-12-12-1 configuration with ROOT. Line thicknesses indicate neural weight / strength of neural connection.
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PROOF
PROOF is an extension to ROOT which allows the parallel analysis of large ROOT trees. The large data sets produced by present and future high energy physics and other experiments makes it a very interesting and useful data analysis tool. PROOF can be used on a set of inhomogenous workstations or in a homogenous cluster environment. It uses a master slave architecture with possible layers of submasters and load balancing through pull mode task distribution. It has been designed to be used on systems ranging from single or multi core workstations to large clusters with O(100) CPUs or even collections of clusters connected with grid middleware. The best performance is obtained when the data set to be analyzed is distributed over the local disks of the cluster nodes. Good scaling has been reported for over 70 CPUs [10]. When PROOF is used on a system with a batch queue system some work is needed to optimize the resource utilization. In the simplest case a static configuration of the slave nodes can be set up, but this clearly is not optimal in terms of resource usage. The integration of PROOF with a batch queue system can be implemented as a batch queue that accepts standard jobs or PROOF sessions or by asking the batch queue system for a list of free CPUs and then constructing dynamically the needed slave configuration file and reserving the slaves taken into use from the batch queue system. Work on PROOF is progressing towards dynamic slave allocation and we report here on our implementation.
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ProofBench is a benchmarking tool distributed together with PROOF for performance studies [11]. We report here on performance results obtained for the clusters at our disposal.
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MLP results
In this section we compare the performance of the new classifiers studied in this work, with the results obtained in an earlier study [2].
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Conclusions
ROOT and PROOF have been installed on two Linux clusters and integrated with the NPACI Rocks Linux Cluster Distribution and the Sun N1 Grid Engine batch queue system. We report on the performance of PROOF. These tools have enabled us to study the physics performance of the optimized neural network classifiers developed in this work.
References 1. Heikkinen, A., Lind´en, T.: Validation of GEANT4 Bertini cascade nuclide production using parallel Root facility. Proc. of Computing in High Energy and Nuclear Physics 2006, 13 – 17 January 2006, Mumbai, India (In press) 2. Heikkinen, A., Lehti, S.: Tagging b jets associated with heavy neutral MSSM Higgs bosons. NIM A (In press) 3. Rademakers, F., Goto, M., Canal, P., Brun, R.: ROOT Status and Future Developments. arXiv: cs.SE/0306078 4. Ganis, G., et al.: PROOF – The Parallel ROOT Facility. Proc. of Computing in High Energy and Nuclear Physics 2006, 13 – 17 January 2006, Mumbai, India (In press) 5. Papadopoulos, P., Katz, M., Bruno, G.: NPACI Rocks: Tools and Techniques for Easily Deploying Manageable Linux Clusters. Concurrency Computat: Pract. Exper. 2002; 00:1-20 6. The list of the 500 fastest computers according to the Linpack benchmark, http://www.top500.org/ 7. The homepage of NPACI Rocks cluster distribution, http://www.rocksclusters.org/ 8. Segneri, G., Palla, F.: Lifetime based b-tagging with CMS. CMS NOTE 2002/046 9. Weiser, C., A Combined Secondary Vertex Based B-Tagging Algorithm in CMS. CMS NOTE 2006/014 10. Gonz´ alez Caballero, I., Cano, D., Marco, R., Cuevas, J.: Prototype of a parallel analysis system for CMS using PROOF. Proc. of Computing in High Energy and Nuclear Physics 2006, 13 – 17 January 2006, Mumbai, India (In press) 11. Ballintijn, M., Roland, G., Gulbrandsen, K., Brun, R., Rademakers, F., Canal, P.: Super scaling PROOF to very large clusters. Proc. of Computing in High Energy and Nuclear Physics 2004, 27 September - 1 October 2004, Interlaken, Switzerland, CERN-2005-002 Vol. 1. (2005)
