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https://www.readbyqxmd.com/read/28692677/fux-sim-implementation-of-a-fast-universal-simulation-reconstruction-framework-for-x-ray-systems
#1
Monica Abella, Estefania Serrano, Javier Garcia-Blas, Ines García, Claudia de Molina, Jesus Carretero, Manuel Desco
The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden. The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented...
2017: PloS One
https://www.readbyqxmd.com/read/28685445/a-new-method-based-on-graphics-processing-units-for-fast-near-infrared-optical-tomography
#2
Jingjing Jiang, Linda Ahnen, Alexander Kalyanov, Scott Lindner, Martin Wolf, Salvador Sanchez Majos
The accuracy of images obtained by Diffuse Optical Tomography (DOT) could be substantially increased by the newly developed time resolved (TR) cameras. These devices result in unprecedented data volumes, which present a challenge to conventional image reconstruction techniques. In addition, many clinical applications require taking photons in air regions like the trachea into account, where the diffusion model fails. Image reconstruction techniques based on photon tracking are mandatory in those cases but have not been implemented so far due to computing demands...
2017: Advances in Experimental Medicine and Biology
https://www.readbyqxmd.com/read/28680387/event-driven-random-back-propagation-enabling-neuromorphic-deep-learning-machines
#3
Emre O Neftci, Charles Augustine, Somnath Paul, Georgios Detorakis
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28666314/gpu-powered-model-analysis-with-pysb-cupsoda
#4
Leonard A Harris, Marco S Nobile, James C Pino, Alexander L R Lubbock, Daniela Besozzi, Giancarlo Mauri, Paolo Cazzaniga, Carlos F Lopez
Summary: A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework...
June 28, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28663861/autofocus-method-for-automated-microscopy-using-embedded-gpus
#5
J M Castillo-Secilla, M Saval-Calvo, L Medina-Valdès, S Cuenca-Asensi, A Martínez-Álvarez, C Sánchez, G Cristóbal
In this paper we present a method for autofocusing images of sputum smears taken from a microscope which combines the finding of the optimal focus distance with an algorithm for extending the depth of field (EDoF). Our multifocus fusion method produces an unique image where all the relevant objects of the analyzed scene are well focused, independently to their distance to the sensor. This process is computationally expensive which makes unfeasible its automation using traditional embedded processors. For this purpose a low-cost optimized implementation is proposed using limited resources embedded GPU integrated on cutting-edge NVIDIA system on chip...
March 1, 2017: Biomedical Optics Express
https://www.readbyqxmd.com/read/28659654/real-time-implementation-of-anti-scatter-grid-artifact-elimination-method-for-high-resolution-x-ray-imaging-cmos-detectors-using-graphics-processing-units-gpus
#6
R Rana, S V Setlur Nagesh, D R Bednarek, S Rudin
Scatter is one of the most important factors effecting image quality in radiography. One of the best scatter reduction methods in dynamic imaging is an anti-scatter grid. However, when used with high resolution imaging detectors these grids may leave grid-line artifacts with increasing severity as detector resolution improves. The presence of such artifacts can mask important details in the image and degrade image quality. We have previously demonstrated that, in order to remove these artifacts, one must first subtract the residual scatter that penetrates through the grid followed by dividing out a reference grid image; however, this correction must be done fast so that corrected images can be provided in real-time to clinicians...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28658298/bayesian-lasso-and-multinomial-logistic-regression-on-gpu
#7
Rok Češnovar, Erik Štrumbelj
We describe an efficient Bayesian parallel GPU implementation of two classic statistical models-the Lasso and multinomial logistic regression. We focus on parallelizing the key components: matrix multiplication, matrix inversion, and sampling from the full conditionals. Our GPU implementations of Bayesian Lasso and multinomial logistic regression achieve 100-fold speedups on mid-level and high-end GPUs. Substantial speedups of 25 fold can also be achieved on older and lower end GPUs. Samplers are implemented in OpenCL and can be used on any type of GPU and other types of computational units, thereby being convenient and advantageous in practice compared to related work...
2017: PloS One
https://www.readbyqxmd.com/read/28644816/fast-and-accurate-poisson-denoising-with-trainable-nonlinear-diffusion
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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...
July 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
#17
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
#18
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
#19
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
#20
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
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