Andrew L Beam, Alison Motsinger-Reif, Jon Doyle
BACKGROUND: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. RESULTS: A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing […]
Marvin Damschen, Christian Plessl
This paper introduces Binary Acceleration At Runtime (BAAR), an easy-to-use on-the-fly binary acceleration mechanism which aims to tackle the problem of enabling existent software to automatically utilize accelerators at runtime. BAAR is based on the LLVM Compiler Infrastructure and has a client-server architecture. The client runs the program to be accelerated in an environment which […]
Nhat Tan Nguyen Thanh
Communication remains a significant barrier to scalability on distributed-memory systems. At present, the trend in architectural system design, which focuses on enhancing node performance, exacerbates the communication problem, since the relative cost of communication grows as the computation rate increases. This problem will be more pronounced at the exascale, where computational rates will be orders […]
Hannes Vogt, Mario Schrock
We adopt CUDA-capable Graphic Processing Units (GPUs) for Landau, Coulomb and maximally Abelian gauge fixing in 3+1 dimensional SU(3) and SU(2) lattice gauge field theories. A combination of simulated annealing and overrelaxation is used to aim for the global maximum of the gauge functional. We use a fine grained degree of parallelism to achieve the […]
Weiguang Ding, Ruoyan Wang, Fei Mao, Graham Taylor
In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs. Our performance on 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014) run on 1 GPU. To the best of our knowledge, this is the first open-source Python-based AlexNet implementation […]
Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling
With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to […]
Sagar Venkatesh Gubbi, Chandra Sekhar Seelamantula
Image denoising is a classical problem in image processing and has applications in areas ranging from photography to medical imaging. In this paper, we examine the denoising performance of an optimized spatially-varying Gaussian filter. The parameters of the Gaussian filter are tuned by optimizing a mean squared error estimate which is similar Stein’s Unbiased Risk […]
Izumi Mizuno, Seiji Kameno, Amane Kano, Makoto Kuroo, Fumitaka Nakamura, Noriyuki Kawaguchi, Katsunori M. Shibata, Seisuke Kuji, Nario Kuno
We have developed a software-based polarization spectrometer, PolariS, to acquire full-Stokes spectra with a very high spectral resolution of 61 Hz. The primary aim of PolariS is to measure the magnetic fields in dense star-forming cores by detecting the Zeeman splitting of molecular emission lines. The spectrometer consists of a commercially available digital sampler and […]
Mahdi S. Mohammadi, Mehdi Rezaeian
Scale Invariant Feature Transform (SIFT) is a popular image feature extraction algorithm. SIFT’s features are invariant to many image related variables including scale and change in viewpoint. Despite its broad capabilities, it is computationally expensive. This characteristic makes it hard for researchers to use SIFT in their works especially in real time application. This is […]
Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, Edgar Isaac Hiroshi Leon Saiki
Over the past years there has been significant enthusiasm for development of parallel computing on Graphics Processing Units (GPU) which have now become powerful and affordable hardware equipping data centers and research clusters. Our earlier research has explored the ways to exploit the parallel compute performance of the GPU along the CPU in the same […]
Naresh Balaji, Esin Yavuz, Thomas Nowotny
Simulation of spiking neural networks has been traditionally done on high-performance supercomputers or large-scale clusters. Utilizing the parallel nature of neural network computation algorithms, GeNN (GPU Enhanced Neural Network) provides a simulation environment that performs on General Purpose NVIDIA GPUs with a code generation based approach. GeNN allows the users to design and simulate neural […]
Nathan Yong Seng Chong
This thesis is about scalable formal verification techniques for software. A verification technique is scalable if it is able to scale to reasoning about real (rather than synthetic or toy) programs. Scalable verification techniques are essential for practical program verifiers. In this work, we consider three key characteristics of scalability: precision, performance and automation. We […]
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Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

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