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Automatic Tuning for a Systemic Model of Banking Originated Losses (SYMBOL) Tool on Multicore

Authors: Ronal Muresano, Andrea Pagano


Nowadays, the mathematical/statistical applications are developed with more complexity and accuracy. However, these precisions and complexities have brought as result that applications need more computational power in order to be executed faster. In this sense, the multicore environments are playing an important role to improve and to optimize the execution time of these applications. These environments allow us the inclusion of more parallelism inside the node. However, to take advantage of this parallelism is not an easy task, because we have to deal with some problems such as: cores communications, data locality, memory sizes (cache and RAM), synchronizations, data dependencies on the model, etc. These issues are becoming more important when we wish to improve the application’s performance and scalability. Hence, this paper describes an optimization method developed for Systemic Model of Banking Originated Losses (SYMBOL) tool developed by the European Commission, which is based on analyzing the application's weakness in order to exploit the advantages of the multicore. All these improvements are done in an automatic and transparent manner with the aim of improving the performance metrics of our tool. Finally, experimental evaluations show the effectiveness of our new optimized version, in which we have achieved a considerable improvement on the execution time. The time has been reduced around 96% for the best case tested, between the original serial version and the automatic parallel version.

Keywords: algorithm optimization, bank failures, OpenMP, parallel techniques, statistical tool

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[1] Michailidis, P., Margaritis, K. .Efficient Multi-Core Computations in Computational Statistics and Econometrics, IEEE 15th Int.Conference on Computational Science and Engineering (CSE), pp.267274.
[2] De Lisa R., Zedda S., Vallascas F., Campolongo F., Marchesi M., 2011,Modelling Deposit Insurance Scheme losses in a Basel 2 framework, Journal of Financial Services Research, Volume: 40 Issue: 3 pp.123-141
[3] Vasicek O. A., 2002, Loan portfolio value, Risk per view/risk/technical/2002/1202 loan.pdf
[4] Merton R.C., 1974, On the pricing of corporate debt: the risk structureof interest rates, Journal of Finance, 29, 449-470
[5] Basel Committee on Banking Supervision, 2005, An Explanatory Noteon the Basel II IRB Risk Weight Functions
[6] Basel Committee on Banking Supervision, 2006, International Convergence of Capital Measurement and Capital Standards
[7] Basel Committee on Banking Supervision, 2010 rev 2011, A global regulatory framework for more resilient banks and banking systems
[8] Sironi A., Zazzara C., 2004, Applying Credit Risk Models to Deposit Insurance Pricing: Empirical Evidence from the Italian Banking System, Journal of International Banking Regulation, 6(1)
[9] James C., 1991, The Loss Realized in Bank Failures, Journal of Finance,46, 1223-42
[10] Mistrulli P.E., 2007, Assessing Financial Contagion in the Interbank Market: Maximum Entropy versus Observed Interbank Lending Patterns, Bank of Italy Working Papers n. 641
[11] Upper C., Worms A., 2004, Estimating Bilateral Exposures in the German Interbank Market: Is there Danger of Contagion?, European Economic Review, 8, 827-849
[12] Zedda S., Cannas G., Galliani C., De Lisa R., 2012, The role of contagion in financial crises: an uncertainty test on interbank patterns, EUR Report 25287, ISSN 1831-9424, ISBN 978-92-79-23849-9 95/1/lbna25287enn.pdf
[13] European Commission, Directorate-General for Economic and Financial Affairs, 2011, Public finances in EMU 2011, European Economy 3 2011 economy/2011/pdf/ee-2011-3 en.pdf
[14] European Commission, Directorate-General for Economic and Financial Affairs, 2012, Fiscal Sustainability Report, European Economy 8— 2012 economy/2012/pdf/ee-2012-8 en.pdf
[15] De Rose C., Fernandes P., Lima A, Sales A. and Webber, 2011, Exploiting Multi-core Architectures in Clusters for Enhancing the Performance of the Parallel Bootstrap Simulation Algorithm, IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), pp 1442-1451
[16] OpenMP Architecture Review Board, 2013, OpenMP Application Program Interface
[17] Galassi M, Davies J, Theiler J, Brian G, Jungman G., Alken P., Booth M., Rossi F., 2013, GNU Scientic Library Reference Manual,
[18] Faria Nuno, Silva Rui and Sobral Joao, 2013, Impact of Data Structure Layout on Performance, 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 117- 120,Ireland
[19] Davidson, Jack W., Jinturkar, Sanjay, 2001, An Aggressive Approach to Loop Unrolling, Technical Report, University of Virginia, USA
[20] Message Passing Interface Forum, 2012, MPI: A Message-Passing Interface Standard Version 3.0 Technical report, 2012