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//		HEART WALL DESCRIPTION
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// The Heart Wall application tracks the movement of a mouse heart over a sequence of 104 609x590 ultrasound images to record response to the stimulus. 
// In its initial stage, the program performs image processing operations on the first image to detect initial, partial shapes of inner and outer heart walls. 
// These operations include: edge detection, SRAD despeckling (also part of Rodinia suite), morphological transformation and dilation. In order to reconstruct 
// approximated full shapes of heart walls, the program generates ellipses that are superimposed over the image and sampled to mark points on the heart walls 
// (Hough Search). In its final stage (Heart Wall Tracking presented here), program tracks movement of surfaces by detecting the movement of image areas under 
// sample points as the shapes of the heart walls change throughout the sequence of images.

// For more information, see:
// Papers:
// [1] L. G. Szafaryn, K. Skadron, and J. J. Saucerman. "Experiences Accelerating MATLAB Systems Biology Applications." In Proceedings of the Workshop on Biomedicine 
// in Computing: Systems, Architectures, and Circuits (BiC) 2009, in conjunction with the 36th IEEE/ACM International Symposium on Computer Architecture (ISCA), 
// June 2009. <http://www.cs.virginia.edu/~skadron/Papers/BiC09.pdf>
// [2] Y. Yu, S. Acton, Speckle reducing anisotropic diffusion, IEEE Transactions on Image Processing 11(11)(2002) 1260-1270 <http://people.virginia.edu/~sc5nf/
// 01097762.pdf>
// Presentation Slides:<br>
// [3] L. G. Szafaryn, K. Skadron. "Experiences Accelerating MATLAB Systems Biology Applications - Heart Wall Tracking". <http://www.cs.virginia.edu/~lgs9a/rodinia/
// heart_wall/tracking/tracking.ppt>

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//		SRAD DESCIPTION
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// SRAD is one of the first stages of the Heart Wall application. SRAD (Speckle Reducing Anisotropic Diffusion) is a diffusion method for ultrasonic and radar imaging 
// applications based on partial differential equations (PDEs). It is used to remove locally correlated noise, known as speckles, without destroying important image 
// features. SRAD consists of several pieces of work: image extraction, continuous iterations over the image (preparation, reduction, statistics, computation 1 and 
// computation 2) and image compression. The sequential dependency between all of these stages requires synchronization after each stage (because each stage 
// operates on the entire image). 

// Partitioning of the working set between caches and avoiding of cache trashing contribute to the performance. In CUDA version, each stage is a separate kernel 
// (due to synchronization requirements) that operates on data already residing in GPU memory. In order to improve GPU performance data was transferred to GPU at 
// the beginning of the code and then transferred back to CPU after all of the computation stages were completed in GPU. Some of the kernels use GPU shared memory 
// for additional improvement in performance. Speedup achievable with CUDA version depends on the image size (up to the point where GPU saturates).
 
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//		USE
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// This is the OpenMP version of SRAD code.

// Input image is generated by expanding the original image (image.pgm) via concatenating its parts. The original image needs to be located in the same folder as source 
// files.

// The following are the command parameters to the application:
// 1) Number of iterations. Needs to be integer > 0.
// 2) Saturation coefficient. Needs to be float > 0.
// 3) Number of rows in the input image. Needs to be integer > 0.
// 4) Number of columns in the input image. Needs to be integer > 0.
// 5) Number of threads. Needs to be integer > 0.
// Example:
// a.out 100 0.5 502 458 4
//
// for more information see main.c
