@article{(Open Science Index):https://publications.waset.org/pdf/9999603,
	  title     = {Automatic Tuning for a Systemic Model of Banking Originated Losses (SYMBOL) Tool on Multicore},
	  author    = {Ronal Muresano and  Andrea Pagano},
	  country	= {},
	  institution	= {},
	  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.
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {8},
	  number    = {10},
	  year      = {2014},
	  pages     = {3292 - 3302},
	  ee        = {https://publications.waset.org/pdf/9999603},
	  url   	= {https://publications.waset.org/vol/94},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 94, 2014},