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//	INFO
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//	UPDATE
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//	2009.12 Lukasz G. Szafaryn
//		-- converted from MATLAB to CUDA
//	2010.01 Lukasz G. Szafaryn
//		-- arranged, commented
//	2012.05 Lukasz G. Szafaryn
//		-- arranged, commented
//	2012.05 Lukasz G. Szafaryn
//		-- converted from CUDA to OpenCL

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//	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.

// Tracking is the final stage of the Heart Wall application. It takes the positions of heart walls from the first ultrasound image in the sequence as determined by the 
// initial detection stage in the application. Tracking code is implemented in the form of multiple nested loops that process batches of 10 frames and 51 points in each 
// image. Displacement of heart walls is detected by comparing currently processed frame to the template frame which is updated after processing a batch of frames. 
// There is a sequential dependency between processed frames. The processing of each point consist of a large number of small serial steps with interleaved control 
// statements. Each of the steps involves a small amount of computation performed only on a subset of entire image. This stage of the application accounts for almost 
// all of the execution time (the exact ratio depends on the number of ultrasound images). 

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//	PAPERS
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// 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>

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//	DOWNLOAD
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// Rodinia Benchmark Suite page

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//	IMPLEMENTATION-SPECIFIC DESCRIPTION (OpenCL)
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// This is the OpenCL version of Tracking code.

// OpenCL implementation of this code is a classic example of the exploitation of braided parallelism. Processing of sample points is assigned to multiprocessors (TLP), 
// while processing of individual pixels in each sample image is assigned to processors inside each multiprocessor. However, each GPU multiprocessor is usually 
// underutilized because of the limited amount of computation at each computation step. Large size of processed images and lack temporal locality did not allow for 
// utilization of fast shared memory. Also the GPU overhead (data transfer and kernel launch) are significant. In order to provide better speedup, more drastic GPU 
// optimization techniques that sacrificed modularity (in order to include code in one kernel call) were used. These techniques also combined unrelated functions and 
// data transfers in single kernels.

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//	RUNNING THIS CODE
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// The code takes the followint input files that need to be located in the same directory as the source files:
// 1) video file (input.avi)
// 2) text file with parameters (input.txt)

// The following are the command parameters to the application:
// 1) Number of frames to process. Needs to be integer <= to the number of frames in the input file.
// Example:
// ./a.out 104

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//	End
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//	End
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