Phase Retrieval Holography for Particle Measurement With GPU Acceleration

2019 ◽  
Author(s):  
Yohsuke Tanaka ◽  
Hiroki Matsushi ◽  
Shigeru Murata
Keyword(s):  
Operating System ◽  
Execution Time ◽  
Phase Retrieval ◽  
Small Volume ◽  
Graphics Processing Unit ◽  
Gpu Acceleration ◽  
Processing Unit ◽  
Drastic Reduction ◽  
Particle Measurement ◽  
Graphics Processing

Abstract We introduce a graphics processing unit (GPU) acceleration to reconstructing holograms of phase retrieval holography for a drastic reduction of the execution time. We conducted GPU acceleration using the FFT library CUFFT on the GPU chip (GEFORCE GTX 1050, GDDR5 2GB, NVIDIA). We also used Intel Xeon CPU (E5-2690, 2.90GHz, Intel), the memory of 24 GB, and the operating system of Ubuntu 16.04 to compare GPU and CPU. Reconstructed volumes changed from 2562 × 128 voxels to 20482 × 1024 voxel to compare execution times. The ratio of the time of GPU to that of CPUs is constantly higher than 100 times except for small volume. We also demonstrated that GPU acceleration decreased the time by observing falling particles, recorded in 40 frames, from particle feeder. As a result, it is found that the execution time is reduced from 13 hours to 30 minutes.

scholarly journals Zeffiro User Interface for Electromagnetic Brain Imaging: a GPU Accelerated FEM Tool for Forward and Inverse Computations in Matlab

2019 ◽  
Vol 18 (2) ◽  
pp. 237-250 ◽  
Author(s):  
Q. He ◽  
A. Rezaei ◽  
S. Pursiainen
Keyword(s):  
Brain Imaging ◽  
Graphics Processing Unit ◽  
Computing Time ◽  
Synthetic Data ◽  
Gpu Acceleration ◽  
Head Model ◽  
Processing Unit ◽  
Impedance Tomography ◽  
Current Configuration ◽  
Graphics Processing

Abstract This article introduces the Zeffiro interface (ZI) version 2.2 for brain imaging. ZI aims to provide a simple, accessible and multimodal open source platform for finite element method (FEM) based and graphics processing unit (GPU) accelerated forward and inverse computations in the Matlab environment. It allows one to (1) generate a given multi-compartment head model, (2) to evaluate a lead field matrix as well as (3) to invert and analyze a given set of measurements. GPU acceleration is applied in each of the processing stages (1)–(3). In its current configuration, ZI includes forward solvers for electro-/magnetoencephalography (EEG) and linearized electrical impedance tomography (EIT) as well as a set of inverse solvers based on the hierarchical Bayesian model (HBM). We report the results of EEG and EIT inversion tests performed with real and synthetic data, respectively, and demonstrate numerically how the inversion parameters affect the EEG inversion outcome in HBM. The GPU acceleration was found to be essential in the generation of the FE mesh and the LF matrix in order to achieve a reasonable computing time. The code package can be extended in the future based on the directions given in this article.

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.

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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