scholarly journals Compressed sensing reconstruction for whole-heart imaging with 3D radial trajectories: A graphics processing unit implementation

2012 ◽  
Vol 69 (1) ◽  
pp. 91-102 ◽  
Author(s):  
Seunghoon Nam ◽  
Mehmet Akçakaya ◽  
Tamer Basha ◽  
Christian Stehning ◽  
Warren J. Manning ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heart Imaging ◽  
Whole Heart Imaging ◽  
Whole Heart ◽  
Graphics Processing

scholarly journals Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

2020 ◽  
Vol 59 (3) ◽  
pp. 712
Author(s):  
Mingjie Gao ◽  
Guangtao Si ◽  
Yuanyuan Bai ◽  
Lihong V. Wang ◽  
Chengbo Liu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Compressed Sensing ◽  
Total Variation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Graphics Processing

Accelerated Deconvolution of Radio Interferometric Images using Orthogonal Matching Pursuit and Graphics Hardware

2017 ◽  
Vol 06 (04) ◽  
pp. 1750009
Author(s):  
Jonathan Van Belle ◽  
Richard Armstrong ◽  
James Gain
Keyword(s):  
Compressed Sensing ◽  
Graphics Processing Unit ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Rigorous Framework ◽  
Random Measurements ◽  
Graphics Processing ◽  
Sparse Set

Deconvolution of native radio interferometric images constitutes a major computational component of the imaging process. An efficient and robust deconvolution operation is essential for reconstruction of the true sky signal from measured telescopic data. The techniques of compressed sensing provide a mathematically-rigorous framework within which to implement deconvolution of images formed from a sparse set of nearly-random measurements. We present an accelerated implementation of the orthogonal matching pursuit (OMP) algorithm (a compressed sensing method) that makes use of graphics processing unit (GPU) hardware. We show that OMP correctly identifies more sources than CLEAN, identifying up to 82% of the sources in 100 test images, while CLEAN only identifies up to 61% of the sources. In addition, the residual after source extraction is [Formula: see text] times lower for OMP than for CLEAN. Furthermore, the graphics implementation of OMP performs around 23 times faster than a 4-core CPU.

Fast iterative solvers for large compressed-sparse row linear systems on graphics processing unit

2015 ◽  
Vol 10 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Frédéric Magoulès ◽  
Abal-Kassim Cheik Ahamed ◽  
Roman Putanowicz
Keyword(s):  
Linear Systems ◽  
Graphics Processing Unit ◽  
Iterative Solvers ◽  
Processing Unit ◽  
Compressed Sparse Row ◽  
Graphics Processing

Performance Analysis and Optimization of Graphics Processing Unit

2019 ◽  
Author(s):  
Lokendra Singh Umrao ◽  
Jay Prakash Pandey
Keyword(s):  
Performance Analysis ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Graphics Processing

Implementing wide baseline matching algorithms on a graphics processing unit.

2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers
Keyword(s):  
Graphics Processing Unit ◽  
Processing Unit ◽  
Wide Baseline Matching ◽  
Graphics Processing

scholarly journals Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

2021 ◽  
Vol 22 (10) ◽  
pp. 5212
Author(s):  
Andrzej Bak
Keyword(s):  
Molecular Conformation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Diverse Range ◽  
Current State ◽  
Gpu Clusters ◽  
Pharmacophore Hypothesis ◽  
Rising Power ◽  
Graphics Processing ◽  
Ligand Conformation

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Parallelization of Global Sequence Alignment on Graphics Processing Unit

2020 ◽  
Author(s):  
Kailash W. Kalare ◽  
Mohammad S. Obaidat ◽  
Jitendra V. Tembhurne ◽  
Chandrashekhar Meshram ◽  
Kuei-Fang Hsiao
Keyword(s):  
Sequence Alignment ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Graphics Processing

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

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