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Ziming Zhong
Over the past decade, the design of microprocessors has been shifting to a new model where the microprocessor has multiple homogeneous processing units, aka cores, as a result of heat dissipation and energy consumption issues. Meanwhile, the demand for heterogeneity increases in computing systems due to the need for high performance computing in recent years. […]
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Martin R. Oswald, Daniel Cremers
We show that surface normal information allows to significantly improve the accuracy of a spatio-temporal multi-view reconstruction. On one hand, normal information can improve the quality of photometric matching scores. On the other hand, the same normal information can be employed to drive an adaptive anisotropic surface regularization process which better preserves fine details and […]
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Linlin Xu, Giray Okten
GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point in GPU computing. GPU speed-up examples reported in the literature […]
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F. T. Winter, M. A. Clark, R. G. Edwards, B. Joo
Computing platforms equipped with accelerators like GPUs have proven to provide great computational power. However, exploiting such platforms for existing scientific applications is not a trivial task. Current GPU programming frameworks such as CUDA C/C++ require low-level programming from the developer in order to achieve high performance code. As a result porting of applications to […]
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Benjamin C. Johnstone
A new trend in chip multiprocessor (CMP) design is to incorporate graphics processing unit (GPU) cores, making them heterogeneous. GPU cores have a higher bandwidth requirement than CPU cores, as they tend to generate much more memory requests. In order to achieve good performance, there must be sufficient bandwidth between the GPU shader cores and […]
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Raphael Landaverde, Tiansheng Zhang, Ayse K. Coskun, Martin Herbordt
Managing memory between the CPU and GPU is a major challenge in GPU computing. A programming model, Unified Memory Access (UMA), has been recently introduced by Nvidia to simplify the complexities of memory management while claiming good overall performance. In this paper, we investigate this programming model and evaluate its performance and programming model simplifications […]
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Keisuke Konno, Qiang Chen, Hajime Katsuda
Various guidelines for acceleration of MoM by GPU computing are summarized. Acceleration of direct/iterative solver for MoM by using GPU is realized. Quantitative study of computing time shows the performance of each guideline.
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Durlabh Chauhan, Satvir Singh, Sarabjeet Singh, Vijay Kumar Banga
Parallelcomputing is one of significant components of the High Performance Computing (HPC) and is being used to solve problems, which are large and complex in nature. Fuzzy Logic System (FLS) is a problem that becomes computationally intensive with increase in number of inputs and/or fuzzy rules. Running an FLS is highly parallel in nature, therefore, […]
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Sergiy Gogolenko, Zhaojun Bai, Richard Scalettar
We present a block structured orthogonal factorization (BSOF) algorithm and its parallelization for computing the inversion of block p-cyclic matrices.We aim at the high performance on multicores with GPU accelerators. We provide a quantitative performance model for optimal host-device load balance, and validate the model through numerical tests. Benchmarking results show that the parallel BSOF […]
Lan Vu, Hari Sivaraman, Rishi Bidarkar
Graphics Processing Units (GPU) have become important components in high performance computing (HPC) systems for their massively parallel computing capability and energy efficiency. Virtualization technologies are increasingly applied to HPC to reduce administration costs and improve system utilization. However, virtualizing the GPU to support general purpose computing presents many challenges because of the complexity of […]
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Newsha Ardalani, Karthikeyan Sankaralingam, Xiaojin Zhu
Heterogeneous processing using GPUs is here to stay and today spans mobile devices, laptops, and supercomputers. Although modern software development frameworks like OpenCL and CUDA serve as a high productivity environment, software development for GPUs is time consuming. First, much work needs to be done to restructure software and data organization to match the GPU’s […]
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Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and […]
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