1,721,009 research outputs found
A sparse nonsymmetric eigensolver for distributed memory architectures
No full textIn this work, we propose an efficient parallel implementation of the nonsymmetric
block Lanczos algorithm for the computation of few extreme eigenvalues, and
corresponding eigenvectors, of real nonhermitian matrices for distributed memory
multicomputers. The reorganisation of the block Lanczos algorithm implemented
allows to exploit a coarse-grained parallelism and to harness the computational power
of the target architectures. The computational kernels of the algorithm are matrix–
matrix multiplications, with dense and sparse factors, QR factorisation and singular
value decomposition. To reduce the total amount of communication involved in the
matrix–matrix multiplication with a sparse factor, we substitute each matrix appearing
in the algorithm with its transpose. Then, we develop an efficient parallelisation of
the matrix–matrix multiplication when the second factor is sparse. Some other linear
algebra operations are performed using ScaLAPACK library. The parallel eigensolver
has been tested on a cluster of PCs. All reported results show the proposed algorithm is
efficient on the target architectures for problems of adequate dimension
A parallel computational kernel for sparse nonsymmetric eigenvalue problems on multicomputers
No full textThe aim of this paper is to show an effective reorganization of the
nonsymmetric block lanczos algorithm efficient, portable and scalable for multiple
instructions multiple data (MIMD) distributed memory message passing
architectures. Basic operations implemented here are matrix-matrix multiplications,
eventually with a transposed and a sparse factor, LU factorisation and
triangular systems solution. Since the communication overhead of the algorithm
inhibits an efficient parallel implementation, we propose a reorganization of the
block algorithm which reduces the total amount of communication involved in
linear algebra operations. Then, we develop an efficient parallelization of the
matrix-matrix multiplication when one of the factor is sparse. Some other linear
algebra operations are performed using ScaLAPACK library. The parallel
eigensolver has been tested on a cluster of PCs. All reported results show the
proposed algorithm is efficient and scalable on the target architectures for problems
of adequate dimension, and it can be the computational kernel of a robust software for large sparse eigenvalue problems
Hybrid MPI/openMP application on multicore architectures: the case of profit-sharing life insurance policies valuation
No full textThe DISAR (Dynamic Investment Strategy with Accounting Rules) system - an Asset-Liability Management software for monitoring portfolios of life insurance policies - has been proven to be extremely efficient on a grid of conventional computers. However, when executed on multicore architectures, it is fundamental to face new challenges, due to the machine characteristics, in order to improve the performance of the code. Further, since in the future an increasing number of cores per-chip - tens and even hundreds - and smaller per-core resources, as memory, are expected, it seems necessary, in the implementation of very large-scale financial applications, to employ an hybrid programming model which uses OpenMP for parallelization inside the node and MPI for message passing between the nodes. We discuss our experiences on two different multicore architectures - an UMA machine and a NUMA one - and we present a set of techniques and software tools that we implement to face the associated problems, including thread binding and correct memory association. We present results using both pure MPI and various hybrid MPI/OpenMP models
Relevant applications of Monte Carlo simulation in Solvency II
No full textThe definition of solvency for insurance companies, within the European Union, is currently being revised as part of Solvency II Directive. The new definition induces revolutionary changes in the logic of control and expands the responsibilities in business management. The rationale of the fundamental measures of the Directive cannot be understood without reference to probability distribution functions. Many insurers are struggling with the realisation of a so-called “internal model” to assess risks and determine the overall solvency needs, as requested by the Directive. The quantitative assessment of the solvency position of an insurer relies on Monte Carlo simulation, in particular on nested Monte Carlo simulation that produces very hard computational and technological problems to deal with. In this paper, we address methodological and computational issues of an “internal model” designing a tractable formulation of the very complex expectations resulting from the “market-consistent” valuation of fundamental measures, such as Technical Provisions, Solvency Capital Requirement and Probability Distribution Forecast, in the solvency assessment of life insurance companies. We illustrate the software and technological solutions adopted to integrate the Disar system—an asset–liability computational system for monitoring life insurance policies—in advanced computing environments, thus meeting the demand for high computing performance that makes feasible the calculation process of the solvency measures covered by the Directive
Relevant applications of Monte Carlo simulation in Solvency II
No full textThe definition of solvency for insurance companies, within the European Union, is currently being revised as part of Solvency II Directive. The new definition induces revolutionary changes in the logic of control and expands the responsibilities in business management. The rationale of the fundamental measures of the Directive cannot be understood without reference to probability distribution functions. Many insurers are struggling with the realisation of a so-called “internal model” to assess risks and determine the overall solvency needs, as requested by the Directive. The quantitative assessment of the solvency position of an insurer relies on Monte Carlo simulation, in particular on nested Monte Carlo simulation that produces very hard computational and technological problems to deal with. In this paper, we address methodological and computational issues of an “internal model” designing a tractable formulation of the very complex expectations resulting from the “market-consistent” valuation of fundamental measures, such as Technical Provisions, Solvency Capital Requirement and Probability Distribution Forecast, in the solvency assessment of life insurance companies. We illustrate the software and technological solutions adopted to integrate the Disar system—an asset–liability computational system for monitoring life insurance policies—in advanced computing environments, thus meeting the demand for high computing performance that makes feasible the calculation process of the solvency measures covered by the Directive
Time-series forecasting of mortality rates using deep learning
No full textThe time-series nature of mortality rates lends itself to processing through
neural networks that are specialized to deal with sequential data, such
as recurrent and convolutional networks. The aim of this work is to show
how the structure of the Lee–Carter model can be generalized using a
relatively simple shallow convolutional network model, allowing for its
components to be evaluated in familiar terms. Although deep networks
have been applied successfully in many areas, we find that deep networks
do not lead to an enhanced predictive performance in our approach for
mortality forecasting, compared to the proposed shallow one. Our model
produces highly accurate forecasts on the Human Mortality Database, and,
without further modification, generalizes well to the United States Mortality
Database
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