Intel® Array Building Blocks Code Samples

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Intel® Software Network

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	May 11, 2011 12:00 AM PDT

Overview

We provide code samples that illustrate the use of Intel® Array Building Blocks (Intel® ArBB) in various workloads including those often used for financial services, graphics, image processing, medical imaging and more. The sample applications provide the most direct way to determine:

Whether the software is working on your system
How you can use the different language constructs (for example, operators, functions, facilities and so on)
How to write code and create an application

Installation

The code samples are contained in the installation package and by default are installed to

Windows* directory C:\Program Files\Intel\arbb\<version>\samples
Linux* directory /opt/intel/arbb/<version>/samples

where <version> is the version of the product being installed.

Building and Running the Samples

On Windows*, open one of the Microsoft* Visual Studio* solution (.sln) files located in the samples folder. For examples, if you use Visual Studio 2005, double-click the file C:\Program Files\Intel\arbb\<version>\samples\samples-vs05.sln.

Select a configuration (the default setting is Debug - Win32 configuration)
From the Build menu, select Build Solution to build the entire solution, which consists of individual projects for each sample. Use the Solution Explorer to view projects grouped in folders by category.
In the Solution Explorer, right-click the name of the sample you are interested in and choose the Set as StartUp Project option from the context menu.
Click the Run button to run the selected sample.

On Linux*, use one of the following shell scripts, located in the folder /opt/intel/arbb/<version>/tools, to build and run the samples:

build_run-icc.sh - automatically build and run the sample applications using Intel® C++ Compiler
build_run-gcc.sh - automatically build and run the sample applications using GCC*

Samples are grouped into directories corresponding to application areas (categories) such as <installation_directory>/samples/imaging, <installation_directory>/samples/seismic and so on.

The directory <installation_directory>/samples/common includes shared utility code such as methods for reading image files and displaying performance results.

The table below lists the available samples, with short descriptions and Intel ArBB keywords.

Sample	Category	Description	Intel ArBB Keywords
montecarlo	finance	Monte Carlo simulation to estimate the price of a set of European ("call") options	dense, bind, call, mul_scan, rotate, _for, _end_for, add_reduce, replace, exp, log, sqrt, cos
sobel	imaging	Sobel is an edge detection operator for a 2D image.	dense, bind, call, shift, map, neighbor
convolution	imaging	Convolution is an example of 1D, 2D and 3D edge enhancement.	auto_closure, neighbor, clamp, map, dense, bind, capture
mandelbrot	graphics	The Mandelbrot set is produced by evaluating a complex quadratic recurrence within a given domain.	dense, bind, map, call, position, as, _while,_end_while,_if,_end_if, break
kirchhoff	seismic	Kirchhoff migration is applied to a set of traces to reconstruct a sub-surface image.	dense, bind, map, call, position, array, _for,_end_for, sqrt
matrix_vector	math	Single-precision matrix-vector multiplication.	dense, bind, call, repeat_row, add_reduce
math	Single-precision sparse matrix-vector multiplication.	dense, bind, call, repeat_row, add_reduce
fft	math	Computes the fast Fourier transform of a complex 2-dimensional input	std::complex, dense, indices, log, map, cos, sin, _for, section, repeat, cat, replace_col, replace_row
histogram	math	The histogram sample computes the number of pixels at each gray level in a 2D grayscale image	dense, u8, u32, bind, call, add_merge, uncaptured
backprojection	medical	The backprojection algorithm is used for tomographic reconstruction	num_rows, num_cols, num_pages, dense, indices, flatten, _for, _end_for, fill, sin, cos, replace_page, reshape, and shift