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David H. Eberly
An In-Depth, Practical Guide to GPGPU Programming Using Direct3D 11. GPGPU Programming for Games and Science demonstrates how to achieve the following requirements to tackle practical problems in computer science and software engineering: Robustness, Accuracy, Speed, Quality source code that is easily maintained, reusable, and readable. The book primarily addresses programming on a graphics processing […]
Alexander Efremov, Eugenya Karepova, Vladimir Shaydurov, Alexander Vyatkin
A parallel implementation of a method of the semi-Lagrangian type for the advection equation on a hybrid architecture com-putation system is discussed. The difference scheme with variable stencil is constructed on the base of an integral equality between the neighboring time levels. The proposed approach allows one to avoid the Courant-Friedrichs-Lewy restriction on the relation […]
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Erhan Okuyan
Direct volume rendering is widely used in many applications where the inside of a transparent or a partially transparent material should be visualized. We have explored several aspects of the problem. First, we proposed a view-dependent selective refinement scheme in order to reduce the high computational requirements without affecting the image quality significantly. Then, we […]
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Bo Fang
While graphics processing units (GPUs) have gained wide adoption as accelerators for general-purpose applications (GPGPU), the end-to-end reliability implications of their use have not been quantified. Fault injection is a widely used method for evaluating the reliability of applications. However, building a fault injector for GPGPU applications is challenging due to their massive parallelism, which […]
Robest Kessl, Nilothpal Talukder, Pranay Anchuri, Mohammed J. Zaki
Frequent graph mining is an important though computationally hard problem because it requires enumerating possibly an exponential number of candidate subgraph patterns, and checking their presence in a database of graphs. In this paper, we propose a novel approach for parallel graph mining on GPUs, which have emerged as a relatively cheap but powerful architecture […]
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Takazumi Matsumoto, Edward Hung, Man Lung Yiu
Outlier detection, also known as anomaly detection, is a common data mining task in identifying data points that are outside expected patterns in a given dataset. It has useful applications such as network intrusion, system faults, and fraudulent activity. In addition, real world data are uncertain in nature and they may be represented as uncertain […]
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Loren Schwiebert, Eyad Hailat, Kamel Rushaidat, Jason Mick, Jeffrey Potoff
Maximizing the performance potential of the modern day GPU architecture requires judicious utilization of available parallel resources. Although dramatic reductions can often be obtained through straightforward mappings, further performance improvements often require algorithmic redesigns to more closely exploit the target architecture. In this paper, we focus on efficient molecular simulations for the GPU and propose […]
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Wiebe Van Ranst, Joost Vennekens
We present an approximate query answering algorithm for the Probabilistic Logic Programming language CP-logic. It complements existing sampling algorithms by using the rules from body to head instead of in the other direction. We present an implementation in OpenCL, which is able to exploit the multicore architecture of modern GPUs to compute a large number […]
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Mario Mastriani
A quantum Boolean image processing methodology is presented in this work, with special emphasis in image denoising. A new approach for internal image representation is outlined together with two new interfaces: classical-to-quantum and quantum-to-classical. The new quantum-Boolean image denoising called quantum Boolean mean filter (QBMF) works with computational basis states (CBS), exclusively. To achieve this, […]
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Marco Signoretto, Emanuele Frandi, Zahra Karevan, Johan A. K. Suykens
We propose an approach suitable to learn multiple time-varying models jointly and discuss an application in data-driven weather forecasting. The methodology relies on spectral regularization and encodes the typical multi-task learning assumption that models lie near a common low dimensional subspace. The arising optimization problem amounts to estimating a matrix from noisy linear measurements within […]
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Yuan Zhao, Xinchang Zhang, Zhen Zhang, Lu Wang, Yueming Hu
Because Cellular Automata (CA) is a dynamic system with inherent parallelism, many studies are focused on mapping CA to the parallel system in order to obtain high performance computing capability, such as using clusters, supercomputers and networks of computers. But the application of these systems are too expensive and difficult to use on the occasions […]
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Olav Aanes Fagerlund, Takeshi Kitayama, Gaku Hashimoto, Hiroshi Okuda
In the finite element method simulation we often deal with large sparse matrices. Sparse matrix-vector multiplication (SpMV) is of high importance for iterative solvers. During the solver stage, most of the time is in fact spent in the SpMV routine. The SpMV routine is highly memory-bound; the processor spends much time waiting for the needed […]
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