Faculty Publications
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Item GPU implementation of non-local maximum likelihood estimation method for denoising magnetic resonance images(Springer Verlag service@springer.de, 2017) Upadhya, A.H.K.; Talawar, B.; Rajan, J.Magnetic resonance imaging (MRI) is a widely deployed medical imaging technique used for various applications such as neuroimaging, cardiovascular imaging and musculoskeletal imaging. However, MR images degrade in quality due to noise. The magnitude MRI data in the presence of noise generally follows a Rician distribution if acquired with single-coil systems. Several methods are proposed in the literature for denoising MR images corrupted with Rician noise. Amongst the methods proposed in literature for denoising MR images corrupted with Rician noise, the non-local maximum likelihood methods (NLML) and its variants are popular. In spite of the performance and denoising quality, NLML algorithm suffers from a tremendous time complexity O(m3N3) , where m3 and N3 represent the search window and image size, respectively, for a 3D image. This makes the algorithm challenging for deployment in the real-time applications where fast and prompt results are required. A viable solution to this shortcoming would be the application of a data parallel processing framework such as Nvidia CUDA so as to utilize the mutually exclusive and computationally intensive calculations to our advantage. The GPU-based implementation of NLML-based image denoising achieves significant speedup compared to the serial implementation. This research paper describes the first successful attempt to implement a GPU-accelerated version of the NLML algorithm. The main focus of the research was on the parallelization and acceleration of one computationally intensive section of the algorithm so as to demonstrate the execution time improvement through the application of parallel processing concepts on a GPU. Our results suggest the possibility of practical deployment of NLML and its variants for MRI denoising. © 2016, Springer-Verlag Berlin Heidelberg.Item Parallel iterative hill climbing algorithm to solve TSP on GPU(John Wiley and Sons Ltd, 2019) Yelmewad, P.; Talawar, B.Traveling Salesman Problem (TSP) is an NP-hard combinatorial optimization problem. Heuristic algorithms provide satisfactory solutions to large instance TSP in a reasonable amount of time. However, heuristic methods result in suboptimal solutions as they do not cover the search space adequately. Sequential heuristic approaches spend significant CPU time in neighborhood generation for large input instances. Neighborhood generation time can be reduced by generating in parallel. GPUs have been shown to be effective in exploiting data and memory level parallelism in large complex problems. This work presents a GPU-based Parallel Iterative Hill Climbing (PIHC) algorithm using the nearest neighborhood heuristic to arrive at near-optimal solutions of large TSPLIB instances in a reasonable amount of time. Multiple construction heuristics approaches, thread mapping strategies, and data structures for TSPLIB instances have been evaluated. We demonstrate improved cost quality on symmetric TSPLIB instances up to 85,900 cities. The PIHC GPU implementation gives up to 193× speedup over its sequential counterpart and up to 979.96× speedup over a state-of-the-art GPU-based TSP solver. The PIHC implementation gives a cost quality with error rate 0.72% in the best case and 8.06% in the worst case. © 2018 John Wiley & Sons, Ltd.