White Papers
Utilization of Parallel Solver Libraries to Solve Structural and Fluid Problems
FEA being commonly used for solving large size problems (millions of Degrees of Freedom) even on desktops, there is a need for FEA Vendors to upgrade the solvers to exploit the multi-core capabilities. The same methodologies like OpenMP that were used for multi processor shared memory computers are equally valid for the MULTICORE systems. It is in this context that the Parallel Math and Matrix libraries provided by the hardware vendors like INTEL, which are fine tuned for performance on their processors and memory configurations, offer a significant advantage. It is profitable to exploit the availability of these libraries to cut short the development time and cost. This approach would allow the FEA Vendors to quickly adapt and exploit the newer hardware and the associated technologies. In this paper we discuss our experience of using the INTEL MATH KERNEL LIBRARY for extending NISA Solvers on to multi core systems.
By Anil Kumbhar, Kiran Chakravarthy, Ramdass Keshavamurthy, G V Rao - Cranes Software
Learn More › [PDF 261 KB]
Fast Fourier Transforms in the Intel Math Kernel Library
This article introduces the readers to the APIs provided by the Intel MKL to invoke Fast Fourier Transforms. The article compares the Intel MKL FFT API to that of FFTW. Intel MKL also provides FFTW-style APIs to make it easy for current users of FFTW link to Intel MKL without changing their code. Please see the Intel MKL documentation for porting FFTW 2.x and FFTW 3.x APIs to Intel MKL.
By Rezaur Rahman – Intel Corporation
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Monte Carlo European Options Pricing
Monte Carlo simulation is one of the recognized numerical tools for pricing derivative securities, particularly flexible and useful for complex models of real markets. The goal of this article is to compare performance advantages and simplicity of using random number generators available in some industrial numerical libraries. For that purpose a simple and well-known Black-Scholes option pricing model, is used as a framework for illustrating the option pricing use. The paper is intended for software developers interested in efficient implementations of Monte Carlo simulations.
By Sergey A. Maidanov – Intel Corporation
Learn More › [PDF 153 KB]
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