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Adapting and Optimizing the Systemic Model of Banking Originated Losses (SYMBOL) Tool to the Multi-core Architecture


  • Ronal Muresano

    () (European Commission, Joint Research Centre (JRC), Institute for the Protection and the Security of the Citizen (IPSC), Financial and Economic Analysis Unit)

  • Andrea Pagano

    () (European Commission, Joint Research Centre (JRC), Institute for the Protection and the Security of the Citizen (IPSC), Financial and Economic Analysis Unit)


Currently, multi-core system is a predominant architecture in the computational word. This gives new possibilities to speedup statistical and numerical simulations, but it also introduce many challenges we need to deal with. In order to improve the performance metrics, we need to consider different key points as: core communications, data locality, dependencies, memory size, etc. This paper describes a series of optimization steps done on the SYMBOL model meant to enhance its performance and scalability. SYMBOL is a micro-funded statistical tool which analyses the consequences of bank failures, taking into account the available safety nets, such as deposit guarantee schemes or resolution funds. However, this tool, in its original version, has some computational weakness, because its execution time grows considerably, when we request to run with large input data (e.g. large banking systems) or if we wish to scale up the value of the stopping criterium, i.e. the number of default scenarios to be considered. Our intention is to develop a tool (extendable to other model having similar characteristics) where a set of serial (e.g. deleting redundancies, loop enrolling, etc.) and parallel strategies (e.g. OpenMP, and GPU programming) come together to obtain shorter execution time and scalability. The tool uses automatic configuration to make the best use of available resources on the basis of the characteristics of the input datasets. Experimental results, done varying the size of the input dataset and the stopping criterium, show a considerable improvement one can obtain by using the new tool, with execution time reduction up to 96 % of with respect to the original serial version.

Suggested Citation

  • Ronal Muresano & Andrea Pagano, 2016. "Adapting and Optimizing the Systemic Model of Banking Originated Losses (SYMBOL) Tool to the Multi-core Architecture," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 253-280, August.
  • Handle: RePEc:kap:compec:v:48:y:2016:i:2:d:10.1007_s10614-015-9509-4
    DOI: 10.1007/s10614-015-9509-4

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    References listed on IDEAS

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    Cited by:

    1. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    2. Parrado-Martínez, Purificación & Gómez-Fernández-Aguado, Pilar & Partal-Ureña, Antonio, 2019. "Factors influencing the European bank’s probability of default: An application of SYMBOL methodology," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 223-240.
    3. Pilar Gómez-Fernández-Aguado & Purificación Parrado-Martínez & Antonio Partal-Ureña, 2018. "Risk Profile Indicators and Spanish Banks’ Probability of Default from a Regulatory Approach," Sustainability, MDPI, Open Access Journal, vol. 10(4), pages 1-16, April.

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