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By Christian de Schryver, Henning Marxen, Stefan Weithoffer, Norbert Wehn (auth.), Wim Vanderbauwhede, Khaled Benkrid (eds.)
High-Performance Computing utilizing FPGA covers the world of excessive functionality reconfigurable computing (HPRC). This e-book offers an outline of architectures, instruments and purposes for High-Performance Reconfigurable Computing (HPRC). FPGAs provide very excessive I/O bandwidth and fine-grained, customized and versatile parallelism and with the ever-increasing computational wishes coupled with the frequency/power wall, the expanding adulthood and functions of FPGAs, and the appearance of multicore processors which has brought on the recognition of parallel computational types. The half on architectures will introduce varied FPGA-based HPC systems: hooked up co-processor HPRC architectures reminiscent of the CHREC’s Novo-G and EPCC’s Maxwell platforms; tightly coupled HRPC architectures, e.g. the express hybrid-core computing device; reconfigurably networked HPRC architectures, e.g. the QPACE process, and standalone HPRC architectures reminiscent of EPFL’s CONFETTI procedure. The half on instruments will concentrate on high-level programming ways for HPRC, with chapters on C-to-Gate instruments (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical instruments (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for instance OpenCL, Microsoft’s Kiwi and Alchemy projects). The half on functions will current case from a number of program domain names the place HPRC has been used effectively, corresponding to Bioinformatics and Computational Biology; monetary Computing; Stencil computations; details retrieval; Lattice QCD; Astrophysics simulations; climate and weather modeling.
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1344707 8. S. com/2008/09/18/credit-default-swaps/. Accessed 28th January 2013 9. L. Heston, A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financ. Stud. 6(2), 327 (1993). 327 10. Q. Jin, W. B. Thomas, Unifying finite difference option-pricing for hardware acceleration, in International Conference on Field Programmable Logic and Applications (FPL), 2011 (IEEE Computer Society, Los Alamitos, USA, 2011), pp. 6–9. ISBN: 978-0-7695-45295.
However, financial computing has not benefited relatively greatly from early developments in high performance computing, as the latter aimed mainly at engineering and weapon design applications. Besides, financial experts were initially focusing on developing mathematical models and computer simulations in order to comprehend the behavior of financial markets and develop risk-management tools. As this effort progressed, the complexity of financial computing applications grew up rapidly. Hence, high performance computing turned out to be very important in the field of finance.
4 Structure of CPDK application The random samples in Brownian motion (often used to model financial option drifts) follow a Normal distribution, or say Gaussian distribution. A generally used method is to produce a set of uniform random samples (over the interval of (0, 1)), and then convert them to Gaussian random numbers. In the following two subsections, we will introduce methods for generating uniform and Gaussian random numbers, respectively. 1 Uniform Random Number Generator Uniform random numbers are sampled from a distribution which has the following probability density function: p(x) = 1 if 0 < x < 1 = 0 otherwise (1) It is a convenient distribution as there are many simple methods to transform uniform samples into samples from other distributions.