Automatic Tuning for a Systemic Model of Banking Originated Losses (SYMBOL) Tool on Multicore
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.
[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
http://www.risk.net/data/Pay 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
http://www.bis.org/bcbs/irbriskweight.pdf
[6] Basel Committee on Banking Supervision, 2006, International
Convergence of Capital Measurement and Capital Standards
http://www.bis.org/publ/bcbs128.pdf
[7] Basel Committee on Banking Supervision, 2010 rev 2011, A global
regulatory framework for more resilient banks and banking systems
http://www.bis.org/publ/bcbs189.pdf
[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
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/256
95/1/lbna25287enn.pdf
[13] European Commission, Directorate-General for Economic and Financial
Affairs, 2011, Public finances in EMU 2011, European Economy 3 2011
http://ec.europa.eu/economyfinance/publications/european
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—
2012http://ec.europa.eu/economyfinance/publications/european
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,
http://www.gnu.org/software/gsl/manual/gsl-ref.pdf
[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
[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
http://www.risk.net/data/Pay 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
http://www.bis.org/bcbs/irbriskweight.pdf
[6] Basel Committee on Banking Supervision, 2006, International
Convergence of Capital Measurement and Capital Standards
http://www.bis.org/publ/bcbs128.pdf
[7] Basel Committee on Banking Supervision, 2010 rev 2011, A global
regulatory framework for more resilient banks and banking systems
http://www.bis.org/publ/bcbs189.pdf
[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
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/256
95/1/lbna25287enn.pdf
[13] European Commission, Directorate-General for Economic and Financial
Affairs, 2011, Public finances in EMU 2011, European Economy 3 2011
http://ec.europa.eu/economyfinance/publications/european
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—
2012http://ec.europa.eu/economyfinance/publications/european
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,
http://www.gnu.org/software/gsl/manual/gsl-ref.pdf
[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
@article{"International Journal of Business, Human and Social Sciences:65520", author = "Ronal Muresano and Andrea Pagano", title = "Automatic Tuning for a Systemic Model of Banking Originated Losses (SYMBOL) Tool on Multicore", abstract = "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.", volume = "8", number = "10", pages = "3221-11", }
