scholarly journals GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays

2019 ◽  
Vol 41 (5) ◽  
pp. 301-316 ◽  
Author(s):  
Seyyed Reza Miri Rostami ◽  
Moein Mozaffarzadeh ◽  
Mohsen Ghaffari-Miab ◽  
Ali Hariri ◽  
Jesse Jokerst
Keyword(s):  
Image Reconstruction ◽  
Parallel Computation ◽  
Graphics Processing Unit ◽  
Light Emitting Diode ◽  
Processing Unit ◽  
Light Emitting ◽  
Central Processing ◽  
Device Architecture ◽  
Pixel Image ◽  
Graphics Processing

Double-stage delay-multiply-and-sum (DS-DMAS) is an algorithm proposed for photoacoustic image reconstruction. The DS-DMAS algorithm offers a higher contrast than conventional delay-and-sum and delay-multiply and-sum but at the expense of higher computational complexity. Here, we utilized a compute unified device architecture (CUDA) graphics processing unit (GPU) parallel computation approach to address the high complexity of the DS-DMAS for photoacoustic image reconstruction generated from a commercial light-emitting diode (LED)–based photoacoustic scanner. In comparison with a single-threaded central processing unit (CPU), the GPU approach increased speeds by nearly 140-fold for 1024 × 1024 pixel image; there was no decrease in accuracy. The proposed implementation makes it possible to reconstruct photoacoustic images with frame rates of 250, 125, and 83.3 when the images are 64 × 64, 128 × 128, and 256 × 256, respectively. Thus, DS-DMAS can be efficiently used in clinical devices when coupled with CUDA GPU parallel computation.

Optimization of K-Means Clustering on Graphics Processing Unit Using Compute Unified Device Architecture

2017 ◽  
Vol 14 (1) ◽  
pp. 789-795
Author(s):  
V Saveetha ◽  
S Sophia
Keyword(s):  
High Performance ◽  
Programming Model ◽  
Graphics Processing Unit ◽  
Direct Access ◽  
Communication Overhead ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Central Processing ◽  
Device Architecture ◽  
Graphics Processing

Parallel data clustering aims at using algorithms and methods to extract knowledge from fat databases in rational time using high performance architectures. The computational challenge faced by cluster analysis due to increasing capacity of data can be overcome by exploiting the power of these architectures. The recent development in parallel power of Graphics Processing Unit enables low cost high performance solutions for general purpose applications. The Compute Unified Device Architecture programming model provides application programming interface methods to handle data proficiently on Graphics Processing Unit for iterative clustering algorithms like K-Means. The existing Graphics Processing Unit based K-Means algorithms highly focus on improvising the speedup of the algorithms and fall short to handle the high time spent on transfer of data between the Central Processing Unit and Graphics Processing Unit. A competent K-Means algorithm is proposed in this paper to lessen the transfer time by introducing a novel approach to check the convergence of the algorithm and utilize the pinned memory for direct access. This algorithm outperforms the other algorithms by maximizing parallelism and utilizing the memory features. The relative speedups and the validity measure for the proposed algorithm is elevated when compared with K-Means on Graphics Processing Unit and K-Means using Flag on Graphics Processing Unit. Thus the planned approach proves that communication overhead can be reduced in K-Means clustering.

scholarly journals Numerical simulation of flattened heat pipe with double heat sources for CPU and GPU cooling application in laptop computers

2020 ◽  
Author(s):  
Wisoot Sanhan ◽  
Kambiz Vafai ◽  
Niti Kammuang-Lue ◽  
Pradit Terdtoon ◽  
Phrut Sakulchangsatjatai
Keyword(s):  
Experimental Data ◽  
Heat Pipe ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heat Sources ◽  
Final Thickness ◽  
Laptop Computers ◽  
Central Processing ◽  
Graphics Processing ◽  
Good Agreement

Abstract An investigation of the effect of the thermal performance of the flattened heat pipe on its double heat sources acting as central processing unit and graphics processing unit in laptop computers is presented in this work. A finite element method is used for predicting the flattening effect of the heat pipe. The cylindrical heat pipe with a diameter of 6 mm and the total length of 200 mm is flattened into three final thicknesses of 2, 3, and 4 mm. The heat pipe is placed under a horizontal configuration and heated with heater 1 and heater 2, 40 W in combination. The numerical model shows good agreement compared with the experimental data with the standard deviation of 1.85%. The results also show that flattening the cylindrical heat pipe to 66.7 and 41.7% of its original diameter could reduce its normalized thermal resistance by 5.2%. The optimized final thickness or the best design final thickness for the heat pipe is found to be 2.5 mm.

scholarly journals Finite element method completely implemented for graphic processor units using parallel algorithm libraries

2017 ◽  
Vol 33 (1) ◽  
pp. 53-66 ◽  
Author(s):  
Franz Pichler ◽  
Gundolf Haase
Keyword(s):  
Finite Element ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Processing Unit ◽  
Time Step ◽  
Device Architecture ◽  
Transient Problems ◽  
Speed Up ◽  
Automotive Batteries ◽  
Graphics Processing

A finite element code is developed in which all of the computationally expensive steps are performed on a graphics processing unit via the THRUST and the PARALUTION libraries. The code focuses on the simulation of transient problems where the repeated computations per time-step create the computational cost. It is used to solve partial and ordinary differential equations as they arise in thermal-runaway simulations of automotive batteries. The speed-up obtained by utilizing the graphics processing unit for every critical step is compared against the single core and the multi-threading solutions which are also supported by the chosen libraries. This way a high total speed-up on the graphics processing unit is achieved without the need for programming a single classical Compute Unified Device Architecture kernel.

scholarly journals Software Polarization Spectrometer "PolariS"

2014 ◽  
Vol 03 (03n04) ◽  
pp. 1450010 ◽  
Author(s):  
Izumi Mizuno ◽  
Seiji Kameno ◽  
Amane Kano ◽  
Makoto Kuroo ◽  
Fumitaka Nakamura ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Graphics Processing Unit ◽  
Zeeman Splitting ◽  
High Spectral Resolution ◽  
Processing Unit ◽  
Analog To Digital ◽  
Star Forming ◽  
Device Architecture ◽  
Graphics Processing ◽  
High Degree

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 a Linux computer. The computer is equipped with a graphics processing unit (GPU) to process FFT and cross-correlation using the Compute Unified Device Architecture (CUDA) library developed by NVIDIA. Thanks to a high degree of precision in quantization of the analog-to-digital converter and arithmetic in the GPU, PolariS offers excellent performances in linearity, dynamic range, sensitivity, bandpass flatness and stability. The software has been released under the MIT License and is available to the public. In this paper, we report the design of PolariS and its performance verified through engineering tests and commissioning observations.

ALGORITHM OF SKELETON-BASED STATIC HAND GESTURE RECOGNITION

2020 ◽  
pp. 13-22
Author(s):  
D. A. Kalina ◽  
R. V. Golovanov ◽  
D. V. Vorotnev
Keyword(s):  
Gesture Recognition ◽  
Graphics Processing Unit ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Processing Unit ◽  
The Novel ◽  
Central Processing ◽  
Graphics Processing ◽  
Artificial Network

We present the monocamera approach of static hand gestures recognition based on skeletonization. The problem of creating skeleton of the human’s hand, as well as body, became solvable a few years ago after inventing so called convolutional pose machines – the novel architecture of artificial neural network. Our solution uses such kind of pretrained convolutional artificial network for extracting hand joints keypoints with further skeleton reconstruction. In this work we also propose special skeleton descriptor with proving its stability and distinguishability in terms of classification. We considered a few widespread machine learning algorithms to build and verify different classifiers. The quality of the classifier’s recognition is estimated using the wellknown Accuracy metric, which identified that classical SVM (Support Vector Machines) with radial basis kernel gives the best results. The testing of the whole system was conducted using public databases containing about 3000 of test images for more than 10 types of gestures. The results of a comparative analysis of the proposed system with existing approaches are demonstrated. It is shown that our gesture recognition system provides better quality in comparison with existing solutions. The performance of the proposed system was estimated for two configurations of standard personal computer: with CPU (Central Processing Unit) only and with GPU (Graphics Processing Unit) in addition where the latest one provides realtime processing with up to 60 frames per second. Thus we demonstrate that the proposed approach can find an application in the practice.

scholarly journals Graphics processing unit implementation of the F-statistic for continuous gravitational wave searches

2021 ◽  
Author(s):  
Liam Dunn ◽  
Patrick Clearwater ◽  
Andrew Melatos ◽  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Processing Unit ◽  
Central Processing ◽  
Long Baseline ◽  
Using Data ◽  
Graphics Processing ◽  
Gpu Implementation

Abstract The F-statistic is a detection statistic used widely in searches for continuous gravitational waves with terrestrial, long-baseline interferometers. A new implementation of the F-statistic is presented which accelerates the existing "resampling" algorithm using graphics processing units (GPUs). The new implementation runs between 10 and 100 times faster than the existing implementation on central processing units without sacrificing numerical accuracy. The utility of the GPU implementation is demonstrated on a pilot narrowband search for four newly discovered millisecond pulsars in the globular cluster Omega Centauri using data from the second Laser Interferometer Gravitational-Wave Observatory observing run. The computational cost is 17:2 GPU-hours using the new implementation, compared to 1092 core-hours with the existing implementation.

scholarly journals Analysis of Fast Fourier Transformations algorithm for CUDA Architecture

2012 ◽  
Vol 53 ◽  
Author(s):  
Beatričė Andziulienė ◽  
Evaldas Žulkas ◽  
Audrius Kuprinavičius
Keyword(s):  
Graphics Processing Unit ◽  
General Purpose ◽  
Fast Fourier Transformation ◽  
Processing Unit ◽  
Data Allocation ◽  
Analysis Method ◽  
Central Processing ◽  
Execution Speed ◽  
Cuda Architecture ◽  
Graphics Processing

In this work Fast Fourier transformation algorithm for general purpose graphics processing unit processing (GPGPU) is discussed. Algorithm structure and individual stages performance were analysed. With performance analysis method algorithm distribution and data allocation possibilities were determined, depending on algorithm stages execution speed and algorithm structure. Ratio between CPU and GPU execution during Fast Fourier transform signal processing was determined using computer-generated data with frequency. When adopting CPU code for CUDA execution, it not becomes more complex, even if stream procesor parallelization and data transfering algorith stages are considered. But central processing unit serial execution).

scholarly journals Paralelização do Algoritmo Floyd-Warshall usando GPU

2013 ◽  
Author(s):  
Roussian R. A. Gaioso ◽  
Walid A. R. Jradi ◽  
Lauro C. M. de Paula ◽  
Wanderley De S. Alencar ◽  
Wellington S. Martins ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing ◽  
Graphics Processing

Este artigo apresenta uma implementação paralela baseada em Graphics Processing Unit (GPU) para o problema da identificação dos caminhos mínimos entre todos os pares de vértices em um grafo. A implementação é baseada no algoritmo Floyd-Warshall e tira o máximo proveito da arquitetura multithreaded das GPUs atuais. Nossa solução reduz a comunicação entre a Central Processing Unit (CPU) e a GPU, melhora a utilização dos Streaming Multiprocessors (SMs) e faz um uso intensivo de acesso aglutinado em memória para otimizar o acesso de dados do grafo. A vantagem da implementação proposta é demonstrada por vários grafos gerados aleatoriamente utilizando a ferramenta GTgraph. Grafos contendo milhares de vértices foram gerados e utilizados nos experimentos. Os resultados mostraram um excelente desempenho em diversos grafos, alcançando ganhos de até 149x, quando comparado com uma implementação sequencial, e superando implementações tradicionais por um fator de quase quatro vezes. Nossos resultados confirmam que implementações baseadas em GPU podem ser viáveis mesmo para algoritmos de grafos cujo acessos à memória e distribuição de trabalho são irregulares e causam dependência de dados.

scholarly journals Modular Microservice based GPU Utilization Manager with Gunicorn

2020 ◽  
pp. 230-237
Keyword(s):  
Performance Monitoring ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
New Era ◽  
Central Processing ◽  
Massively Parallel Computing ◽  
Improved Performance ◽  
Graphics Processing ◽  
Mathematical Operations ◽  
Speed Of Analysis

:Graphics processing unit (GPU) is a computer programmable chip that could perform rapid mathematical operations that can be accelerated with massive parallelism. In the early days, central processing unit (CPU) was responsible for all computations irrespective of whether it is feasible for parallel computation. However, in recent years GPUs are increasingly used for massively parallel computing applications, such as training Deep Neural Networks. GPU’s performance monitoring plays a key role in this new era since GPUs serve an inevitable role in increasing the speed of analysis of the developed system. GPU administration comes in picture to efficiently utilize the GPU when we deal with multiple workloads to run on the same hardware. In this study, various GPUparameters are monitored and help to keep them in safe levels and also to keep the improved performance of the system. This study,

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