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Andrew Lavin
This paper describes maxDNN, a computationally efficient convolution kernel for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3% computational efficiency on typical deep learning network architectures using a single kernel. The design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We only address forward propagation (FPROP) operation of the network, but […]
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Jan Verschelde, Xiangcheng Yu
Numerical continuation methods apply predictor-corrector algorithms to track a solution path defined by a family of systems, the so-called homotopy. The systems we consider are defined by polynomials in several variables with complex coefficients. For larger dimensions and degrees, the numerical conditioning worsens and hardware double precision becomes often insufficient to reach the end of […]
B. R. Schlei
The novel "Volume-Enclosing Surface exTraction Algorithm" (VESTA) generates triangular isosurfaces from computed tomography volumetric images and/or three-dimensional (3D) simulation data. Here, we present various benchmarks for GPU-based code implementations of both VESTA and the current state-of-the-art Marching Cubes Algorithm (MCA). One major result of this study is that VESTA runs significantly faster than the MCA.
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Yun Tian, Bojian Xu
Repeat finding in strings has important applications in subfields such as computational biology. The challenge of finding the longest repeats covering particular string positions was recently proposed and solved by Ileri et al., using a total of the optimal O(n) time and space, where n is the string size. However, their solution can only find […]
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Blesson Varghese
The risk of reinsurance portfolios covering globally occurring natural catastrophes, such as earthquakes and hurricanes, is quantified by employing simulations. These simulations are computationally intensive and require large amounts of data to be processed. The use of many-core hardware accelerators, such as the Intel Xeon Phi and the NVIDIA Graphics Processing Unit (GPU), are desirable […]
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Lukasz Laniewski-Wollk, Jacek Rokicki
In this paper we present a topology optimization technique applicable to a broad range of flow design problems. We propose also a discrete adjoint formulation effective for a wide class of Lattice Boltzmann Methods (LBM). This adjoint formulation is used to calculate sensitivity of the LBM solution to several type of parameters, both global and […]
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Matthew C. Overby
Urban form modifies the microclimate and may trap in heat and pollutants. This causes a rise of energy demands to heat and cool building interiors. Mitigating these effects is a growing concern due to the increasing urbanization of major cities. Researchers, urban planners, and city architects rely on sophisticated simulations to investigate how to reduce […]
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Wilson Wai Lun Fung
Many applications with regular parallelism have been shown to benefit from using Graphics Processing Units (GPUs). However, employing GPUs for applications with irregular parallelism tends to be a risky process, involving significant effort from the programmer and an uncertain amount of performance/efficiency benefit. One known challenge in developing GPU applications with irregular parallelism is the […]
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Ran Rui, Hao Li, Yi-Cheng Tu
Implementing database operations on parallel platforms has gain a lot of momentum in the past decade, due to the increasing popularity of many-core processors. A number of studies have shown the potential of using GPUs to speed up database operations. In this paper, we present empirical evaluations of a state-of-the-art work published in SIGMOD’08 on […]
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Li Huaming, Kang Baosheng
With the development of the simulation technique, deformable cloth simulation has become highly desired. It can be widely used in many fields such as game, animation, virtual surgery, etc. Real-time algorithm is the most urgent bottleneck problem that needs to be solved. This paper introduces a solution to implement deformable simulation of cloth in real […]
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Qi Lyu, Jun Zhu
Long Short-Term Memory (LSTM) is a deep recurrent neural network architecture with high computational complexity. Contrary to the standard practice to train LSTM online with stochastic gradient descent (SGD) methods, we propose a matrix-based batch learning method for LSTM with full Backpropagation Through Time (BPTT). We further solve the state drifting issues as well as […]
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Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, John D. Owens
For large-scale graph analytics on the GPU, the irregularity of data access and control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system, uses a high-level bulk-synchronous abstraction with traversal and computation steps, designed specifically for the GPU. Gunrock couples high […]
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