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For investigations of rapidly moving structures in opaque technical devices ultrafast electron beam X-ray computed tomography (CT) scanners are available at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). Currently, measurement data must be initially downloaded after each CT scan from the scanner to a data processing machine. Afterwards, cross-sectional images are reconstructed. This limits the application...
The maximum common subgraph of two graphs, G1 and G2, is the largest subgraph in G1 that is isomorphic to a subgraph in G2. Finding the maximum common subgraph of two given graphs is known to be a NP-complete problem. An exact solution for the maximum common subgraph problem can be found by an algorithm that transforms the maximum common subgraph problem into a maximal clique enumeration problem....
Noise reduction is often performed at an early stage of the image processing path. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. In this paper, the block-matching and three-dimensional filtering (BM3D) denoising algorithm is implemented on heterogeneous computing platforms using OpenCL and CUDA frameworks...
This paper presents a series of further modifications to the parallel algorithm used for finding digraphs realisations of the characteristic polynomial. What distinguishes the mentioned algorithm from other state-of-the-art solutions is the ability to find a complete set of existing solutions, not just a few of them. Moreover, solutions found tend to be minimal in terms of a rank of matrices created...
Full use of the parallel computation capabilities of present and expected CPUs and CPUs require use of vector extensions. Yet many actors in data flow systems for digital signal processing have internal state (or, equivalently, an edge that loops from the actor back to itself) that impose serial dependencies between actor invocations that make vectorizing across actor invocations impossible. Ideally,...
Outlier detection is a data mining task consisting in the discovery of observations which deviate substantially from the rest of the data, and has many important practical applications. Outlier detection in very large data sets is however computationally very demanding and the size limit of the data that can be elaborated is considerably pushed forward by mixing three ingredients: efficient algorithms,...
We present two new parallel implementations of the ensemble learning methods Random Forests (RF) and Extremely Randomized Trees (ERT), called gpuRF and gpuERT, for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). RF and ERT are two ensemble methods for generating predictive models that are of high importance within machine learning. They...
GPUs (Graphics Processing Units) are designed to solve large data-parallel problems encountered in the fields of image processing, scene rendering, video playback, and gaming. GPUs are therefore designed to handle a higher degree of parallelism as compared to conventional CPUs. GPGPU (General Purpose computing on Graphics Processing Units) enables users to do parallel computing on the graphics hardware...
This paper introduces a parallel algorithm using GPU for computer simulation of Electrocardiogram (ECG) based on a 3-dimensional (3D) whole-heart model. The computer heart model includes approximately 50,000 discrete elements (cell models) inside a torso model represented by 344 nodal points with 684 triangular meshes. Since computational burden for computer simulation of ECGs is considerably heavy,...
This paper elaborates on a new, fresh parallel optimization algorithm specially engineered to run on Graphic Processing Units (GPUs). The underlying operation relates to Systolic Computation. The algorithm, called Systolic Genetic Search (SGS) is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs. The proposed...
GPU has recently gained considerable attention in getting significant performance, for application raging from scientific computing to database sorting and search. General-purpose computing on GPU can easily reduce the execution time but results in an associated increase in the energy consumption. This paper analyzes energy consumption of parallel algorithms executing on GPU and provide a methodology...
Local search (LS) algorithms are among the most powerful techniques for solving computationally hard problems in combinatorial optimization. These algorithms could be viewed as ??walks through neighborhoods?? where the walks are performed by iterative procedures that allow to move from a solution to another one in the solution space. In these heuristics, designing operators to explore large promising...
Modern microprocessors are becoming increasingly parallel devices, and GPUs are at the leading edge of this trend. Designing parallel algorithms for manycore chips like the GPU can present interesting challenges, particularly for computations on sparse data structures. One particularly common example is the collection of sparse matrix solvers and combinatorial graph algorithms that form the core of...
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