SRAD-V12-RODINIA

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).

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, the 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.

Code Snippet
r2 = 501;
c2 = 457;
Nr = 502;
for (iter=0; iter<niter; iter++){                                           
    float sum=0;
    float sum2=0;
    float temp;
    float *image;

    for (i=0; i<=r2; i++){                                
        for (j=0; j<=c2; j++){                             
            tmp   = image[i + Nr*j];                              
      	    sum  += tmp ;                                   
      	    sum2 += tmp*tmp;                                
   	}
    }

}