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https://www.readbyqxmd.com/read/28644816/fast-and-accurate-poisson-denoising-with-trainable-nonlinear-diffusion
#1
Wensen Feng, Peng Qiao, Yunjin Chen
The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts...
June 20, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28636811/performance-evaluation-of-gpu-parallelization-space-time-adaptive-algorithms-and-their-combination-for-simulating-cardiac-electrophysiology
#2
Rafael S Oliveira, Bernardo M Rocha, Denise Burgarelli, Wagner Meira, Christakis Constantinides, Rodrigo Weber Dos Santos
The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. In order to speed up cardiac simulations and to allow more precise and realistic uses, two different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs), and a sophisticated numerical method based on a new space-time adaptive algorithm...
June 21, 2017: International Journal for Numerical Methods in Biomedical Engineering
https://www.readbyqxmd.com/read/28636392/hybrid-cpu-gpu-integral-engine-for-strong-scaling-ab-initio-methods
#3
Jörg Kussmann, Christian Ochsenfeld
We present a parallel integral algorithm for two-electron contributions occurring in Hartree-Fock and hybrid density functional theory that allows for a strong scaling parallelization on inhomogeneous compute clusters. With a particular focus on graphic processing units, we show that our approach allows an efficient use of CPUs and graphics processing units (GPUs) simultaneously, although the different architectures demand conflictive strategies in order to ensure efficient program execution. Furthermore, we present a general strategy to use large basis sets like quadruple-ζ split valence on GPUs and investigate the balance between CPUs and GPUs depending on l-quantum numbers of the corresponding basis functions...
June 21, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28618232/toward-fast-and-accurate-binding-affinity-prediction-with-pmemdgti-an-efficient-implementation-of-gpu-accelerated-thermodynamic-integration
#4
Tai-Sung Lee, Yuan Hu, Brad Sherborne, Zhuyan Guo, Darrin M York
We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.
June 23, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28600826/tinker-openmm-absolute-and-relative-alchemical-free-energies-using-amoeba-on-gpus
#5
Matthew Harger, Daniel Li, Zhi Wang, Kevin Dalby, Louis Lagardère, Jean-Philip Piquemal, Jay Ponder, Pengyu Ren
The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU-based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ∼200-fold improvement in simulation speed over a single CPU core...
June 10, 2017: Journal of Computational Chemistry
https://www.readbyqxmd.com/read/28580909/the-dynamo-package-for-tomography-and-subtomogram-averaging-components-for-matlab-gpu-computing-and-ec2-amazon-web-services
#6
Daniel Castaño-Díez
Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance...
June 1, 2017: Acta Crystallographica. Section D, Structural Biology
https://www.readbyqxmd.com/read/28572719/multi-gpu-acceleration-of-branchless-distance-driven-projection-and-backprojection-for-clinical-helical-ct
#7
Ayan Mitra, David G Politte, Bruce R Whiting, Jeffrey F Williamson, Joseph A O'Sullivan
Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications...
January 2017: Journal of Imaging Science and Technology
https://www.readbyqxmd.com/read/28561575/employing-opencl-to-accelerate-ab-initio-calculations-on-graphics-processing-units
#8
Jörg Kussmann, Christian Ochsenfeld
We present an extension of our graphics processing units (GPU)-accelerated quantum chemistry package to employ OpenCL compute kernels, which can be executed on a wide range of computing devices like CPUs, Intel Xeon Phi, and AMD GPUs. Here, we focus on the use of AMD GPUs and discuss differences as compared to CUDA-based calculations on NVIDIA GPUs. First illustrative timings are presented for hybrid density functional theory calculations using serial as well as parallel compute environments. The results show that AMD GPUs are as fast or faster than comparable NVIDIA GPUs and provide a viable alternative for quantum chemical applications...
May 31, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28508345/deep-monocular-3d-reconstruction-for-assisted-navigation-in-bronchoscopy
#9
Marco Visentini-Scarzanella, Takamasa Sugiura, Toshimitsu Kaneko, Shinichiro Koto
PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architecture that projects input frames onto the domain of CT renderings, thus allowing offline training from patient-specific CT data. METHODS: A fully convolutional network architecture is implemented on GPU and tested on a phantom dataset involving 32 video sequences and [Formula: see text]60k frames with aligned ground truth and renderings, which is made available as the first public dataset for bronchoscopy navigation...
May 15, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28486952/lassie-simulating-large-scale-models-of-biochemical-systems-on-gpus
#10
Andrea Tangherloni, Marco S Nobile, Daniela Besozzi, Giancarlo Mauri, Paolo Cazzaniga
BACKGROUND: Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs...
May 10, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28436877/packing-vertex-data-into-hardware-decompressible-textures
#11
Kin Chung Kwan, Xuemiao Xu, Liang Wan, Tien-Tsin Wong, Wai-Man Pang
Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processing unit (GPUs)...
April 18, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28413955/drug-discovery-and-molecular-dynamics-methods-applications-and-perspective-beyond-the-second-timescale
#12
Gerard Martínez-Rosell, Toni Giorgino, Matt J Harvey, Gianni de Fabritiis
Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry...
April 14, 2017: Current Topics in Medicinal Chemistry
https://www.readbyqxmd.com/read/28411611/screening-methods-for-linear-scaling-short-range-hybrid-calculations-on-cpu-and-gpu-architectures
#13
Matthias Beuerle, Jörg Kussmann, Christian Ochsenfeld
We present screening schemes that allow for efficient, linear-scaling short-range exchange calculations employing Gaussian basis sets for both CPU and GPU architectures. They are based on the LinK [C. Ochsenfeld et al., J. Chem. Phys. 109, 1663 (1998)] and PreLinK [J. Kussmann and C. Ochsenfeld, J. Chem. Phys. 138, 134114 (2013)] methods, but account for the decay introduced by the attenuated Coulomb operator in short-range hybrid density functionals. Furthermore, we discuss the implementation of short-range electron repulsion integrals on GPUs...
April 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28393937/accelerating-direct-quantum-dynamics-using-graphical-processing-units
#14
T J Penfold
Methods using a swarm of Gaussian basis functions to represent the nuclear wavefunction are a very appealing way to solve the time-dependent Schrödinger equation (TDSE) as they avoid the conventional scaling bottleneck of grid-based methods and provide a grid-free trajectory representation of the dynamics understudy. When coupled with direct (on-the-fly) dynamics, these methods offer the ability to simulate quantum dynamics of larger systems in full nuclear configuration space and avoid the requirement of a priori fitting of a potential energy surface...
April 10, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28373894/gerbil-a-fast-and-memory-efficient%C3%A2-k-mer-counter-with-gpu-support
#15
Marius Erbert, Steffen Rechner, Matthias Müller-Hannemann
BACKGROUND: A basic task in bioinformatics is the counting of k-mers in genome sequences. Existing k-mer counting tools are most often optimized for small k < 32 and suffer from excessive memory resource consumption or degrading performance for large k. However, given the technology trend towards long reads of next-generation sequencers, support for large k becomes increasingly important. RESULTS: We present the open source k-mer counting software Gerbil that has been designed for the efficient counting of k-mers for k ≥ 32...
2017: Algorithms for Molecular Biology: AMB
https://www.readbyqxmd.com/read/28344638/the-feasibility-of-genome-scale-biological-network-inference-using-graphics-processing-units
#16
Raghuram Thiagarajan, Amir Alavi, Jagdeep T Podichetty, Jason N Bazil, Daniel A Beard
Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called 'big data' applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications...
2017: Algorithms for Molecular Biology: AMB
https://www.readbyqxmd.com/read/28269151/efficient-rendering-of-digitally-reconstructed-radiographs-on-heterogeneous-computing-architectures-using-central-slice-theorem
#17
Marwan Abdellah, Mohamed Abdallah, Mohamed Alzanati, Ayman Eldeib
Digitally reconstructed radiographs (DRRs) play a significant role in modern clinical radiation therapy. They are used to verify patient alignments during image guided therapies with 2D-3D image registration. The generation of DRRs can be implemented intuitively in O(N3) relying on direct volume rendering (DVR) methods, such as ray marching. This complexity imposes certain limitations on the rendering performance if high quality DRR images are needed. Those DRRs can be alternatively generated in the k-space using the central slice theorem in O(N2logN)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28254085/a-novel-end-to-end-classifier-using-domain-transferred-deep-convolutional-neural-networks-for-biomedical-images
#18
Shuchao Pang, Zhezhou Yu, Mehmet A Orgun
BACKGROUND AND OBJECTIVES: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset...
March 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28251682/new-algorithm-for-tensor-contractions-on-multi-core-cpus-gpus-and-accelerators-enables-ccsd-and-eom-ccsd-calculations-with-over-1000-basis-functions-on-a-single-compute-node
#19
Ilya A Kaliman, Anna I Krylov
A new hardware-agnostic contraction algorithm for tensors of arbitrary symmetry and sparsity is presented. The algorithm is implemented as a stand-alone open-source code libxm. This code is also integrated with general tensor library libtensor and with the Q-Chem quantum-chemistry package. An overview of the algorithm, its implementation, and benchmarks are presented. Similarly to other tensor software, the algorithm exploits efficient matrix multiplication libraries and assumes that tensors are stored in a block-tensor form...
April 30, 2017: Journal of Computational Chemistry
https://www.readbyqxmd.com/read/28241511/a-weighted-spatial-spectral-kernel-rx-algorithm-and-efficient-implementation-on-gpus
#20
Chunhui Zhao, Jiawei Li, Meiling Meng, Xifeng Yao
The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs)...
February 23, 2017: Sensors
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