Intel® Software Network

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
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Threading Applications with the Intel® Compiler 10.0 Professional Editions

Once the decision to thread an application is made, judiciously choosing the right implementation and the right tools can make a big difference to the efficiency of the development process. The Intel® Compiler 10.0 Professional Editions contain all of the tools you need to express the parallelism in your applications. The Intel® compilers support OpenMP* and native OS threads. The Professional Editions also include Intel® Threading Building Blocks and the threaded implementation of Intel® Integrated Performance Primitives and Intel® Math Kernel Library. Together, these software products provide a rich set of tools and technologies to thread applications and take advantage of multi-core platforms.
By Ganesh Rao – Intel Corporation
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From the Data Center to the Desktop: Putting High Performance Computing to Work

In the past, high performance computing (HPC) required costly supercomputers that typically required whole buildings to house them, huge amounts of power and cooling to run, and whole teams of engineers to write programs for them. Today, new power-efficient, multi-core platform technology offers the possibility of using HPC in any environment, from clustered platforms in the enterprise data center to four or eight-processor systems on the researcher, analyst or designer’s desktop. The biggest challenge to creating these new, mainstream HPC systems is developing software that can take advantage of all the power of the new platforms. HPC requires parallel computing that can be complex and costly to develop, debug, deploy and maintain. What will bring HPC to the mainstream are development tools that can automate the process of parallelizing applications, allowing developers to provide value through their domain expertise, and clustering solutions that streamline and simplify the process of building and maintaining compute clusters.
By Microsoft* and Intel Corporation
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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 technical user notes for porting FFTW 2.x and porting 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
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