scholarly journals A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process

2021 ◽  
Vol 11 (17) ◽  
pp. 8083
Author(s):  
Andrzej Gnatowski ◽  
Teodor Niżyński
Keyword(s):  
Parallel Algorithm ◽  
Graphics Processing Unit ◽  
Random Access ◽  
Welding Process ◽  
Mixed Integer ◽  
Sequential Algorithm ◽  
Processing Unit ◽  
Robotic Welding ◽  
Problem Size ◽  
Sequential Method

Welding frames with differing geometries is one of the most crucial stages in the production of high-end bicycles. This paper proposes a parallel algorithm and a mixed integer linear programming formulation for scheduling a two-machine robotic welding station. The time complexity of the introduced parallel method is O(log2n) on an n3-processor Exclusive Read Exclusive Write Parallel Random-Access Machine (EREW PRAM), where n is the problem size. The algorithm is designed to take advantage of modern graphics cards to significantly accelerate the computations. To present the benefits of the parallelization, the algorithm is compared to the state of art sequential method and a solver-based approach. Experimental results show an impressive speedup for larger problem instances—up to 314 on a single Graphics Processing Unit (GPU), compared to a single-threaded CPU execution of the sequential algorithm.

scholarly journals A Multi-GPU Parallel Algorithm in Hypersonic Flow Computations

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Jianqi Lai ◽  
Hua Li ◽  
Zhengyu Tian ◽  
Ye Zhang
Keyword(s):  
Parallel Algorithm ◽  
Hypersonic Flow ◽  
Stokes Equations ◽  
Programming Model ◽  
Graphics Processing Unit ◽  
Three Dimensional ◽  
Flow Characteristics ◽  
Time Discretization ◽  
Processing Unit ◽  
Complex Flow

Computational fluid dynamics (CFD) plays an important role in the optimal design of aircraft and the analysis of complex flow mechanisms in the aerospace domain. The graphics processing unit (GPU) has a strong floating-point operation capability and a high memory bandwidth in data parallelism, which brings great opportunities for CFD. A cell-centred finite volume method is applied to solve three-dimensional compressible Navier–Stokes equations on structured meshes with an upwind AUSM+UP numerical scheme for space discretization, and four-stage Runge–Kutta method is used for time discretization. Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for GPUs, which reduces the complexity of programming. The main purpose of this paper is to design an extremely efficient multi-GPU parallel algorithm based on MPI+CUDA to study the hypersonic flow characteristics. Solutions of hypersonic flow over an aerospace plane model are provided at different Mach numbers. The agreement between numerical computations and experimental measurements is favourable. Acceleration performance of the parallel platform is studied with single GPU, two GPUs, and four GPUs. For single GPU implementation, the speedup reaches 63 for the coarser mesh and 78 for the finest mesh. GPUs are better suited for compute-intensive tasks than traditional CPUs. For multi-GPU parallelization, the speedup of four GPUs reaches 77 for the coarser mesh and 147 for the finest mesh; this is far greater than the acceleration achieved by single GPU and two GPUs. It is prospective to apply the multi-GPU parallel algorithm to hypersonic flow computations.

Modeling and Computing Overlapping Aggregation of Large Data Sequences in Geographic Information Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 20-41
Author(s):  
Driss En-Nejjary ◽  
Francois Pinet ◽  
Myoung-Ah Kang
Keyword(s):  
Information Systems ◽  
Data Transfer ◽  
Graphics Processing Unit ◽  
Large Data ◽  
General Purpose ◽  
Environmental Data ◽  
Acquisition Time ◽  
Processing Unit ◽  
Sequential Method ◽  
Transfer Cost

Recently, in the field of information systems, the acquisition of geo-referenced data has made a huge leap forward in terms of technology. There is a real issue in terms of the data processing optimization, and different research works have been proposed to analyze large geo-referenced datasets based on multi-core approaches. In this article, different methods based on general-purpose logic on graphics processing unit (GPGPU) are modelled and compared to parallelize overlapping aggregations of raster sequences. Our methods are tested on a sequence of rasters representing the evolution of temperature over time for the same region. Each raster corresponds to a different data acquisition time period, and each raster geo-referenced cell is associated with a temperature value. This article proposes optimized methods to calculate the average temperature for the region for all the possible raster subsequences of a determined length, i.e., to calculate overlapping aggregated data summaries. In these aggregations, the same subsets of values are aggregated several times. For example, this type of aggregation can be useful in different environmental data analyses, e.g., to pre-calculate all the average temperatures in a database. The present article highlights a significant increase in performance and shows that the use of GPGPU parallel processing enabled us to run the aggregations up to more than 50 times faster than the sequential method including data transfer cost and more than 200 times faster without data transfer cost.

scholarly journals An Advance Task Manager Application for Android Devices

2019 ◽  
Vol 9 (1) ◽  
pp. 1915-1918
Keyword(s):  
Graphics Processing Unit ◽  
Random Access ◽  
Memory Storage ◽  
Third Party ◽  
Processing Unit ◽  
Mobile Software ◽  
Mobile Industry ◽  
Storage Network ◽  
Graphics Processing ◽  
Efficient Software

The huge growth of the mobile industry leads to more efficient software to be developed in a mobile phone or android platform. the mobile software and games are now powerful to compete with computer’s software but as it seems the problems will also to be carried out from this enhancement.problems in the sense bug or misbehave of any software that can hang your device.so the Task manager would be needed for mobile devices too, where we can monitor what are all the applications are running and the complete package of informa- tion in one single Application. The Task Manager application shows all the device’s active applications, along with information about:task manager will show how much processor power and cores the app is consuming, and theRAM item shows how much memory of RAM the app occupies. You can use Task Manager to end or turn off tasks that are taking too much CPU time or memory or that any bug containing app. You can click applications you want to stop and then click End Apps button. This project will provide the Active applications ( Applications that are currently running on the device), Installed (Applications that have been installed from the Play Store or any third party provider) RAM manager (Random Access Memory), Storage (Available memory storage), Network uses(Amount of internet data used), GPU ( graphics processing unit), Screen time (time spend on app in day), Internet Speed(speed of connected in- ternet source) and Temperature(temperature of the whole device).

scholarly journals GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ronglin Jiang ◽  
Shugang Jiang ◽  
Yu Zhang ◽  
Ying Xu ◽  
Lei Xu ◽  
...  
Keyword(s):  
Message Passing ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Problem Size ◽  
Central Processing ◽  
Execution Speed ◽  
Speedup Ratio ◽  
Electromagnetic Calculations ◽  
Graphics Processing

This paper introduces a (finite difference time domain) FDTD code written in Fortran and CUDA for realistic electromagnetic calculations with parallelization methods of Message Passing Interface (MPI) and Open Multiprocessing (OpenMP). Since both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) resources are utilized, a faster execution speed can be reached compared to a traditional pure GPU code. In our experiments, 64 NVIDIA TESLA K20m GPUs and 64 INTEL XEON E5-2670 CPUs are used to carry out the pure CPU, pure GPU, and CPU + GPU tests. Relative to the pure CPU calculations for the same problems, the speedup ratio achieved by CPU + GPU calculations is around 14. Compared to the pure GPU calculations for the same problems, the CPU + GPU calculations have 7.6%–13.2% performance improvement. Because of the small memory size of GPUs, the FDTD problem size is usually very small. However, this code can enlarge the maximum problem size by 25% without reducing the performance of traditional pure GPU code. Finally, using this code, a microstrip antenna array with16×18elements is calculated and the radiation patterns are compared with the ones of MoM. Results show that there is a well agreement between them.

scholarly journals A Fast Method for Calculation of Marine Gravity Anomaly

2021 ◽  
Vol 11 (3) ◽  
pp. 1265
Author(s):  
Yuan Fang ◽  
Shuiyuan He ◽  
Xiaohong Meng ◽  
Jun Wang ◽  
Yongkang Gan ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Gravity Data ◽  
Gravity Anomalies ◽  
Geoid Height ◽  
Processing Unit ◽  
Fast Method ◽  
Singularity Problem ◽  
Sequential Method ◽  
Marine Gravity ◽  
Graphics Processing

Gravity data have been playing an important role in marine exploration and research. However, obtaining gravity data over an extensive marine area is expensive and inefficient. In reality, marine gravity anomalies are usually calculated from satellite altimetry data. Over the years, numerous methods have been presented for achieving this purpose, most of which are time-consuming due to the integral calculation over a global region and the singularity problem. This paper proposes a fast method for the calculation of marine gravity anomalies. The proposed method introduces a novel scheme to solve the singularity problem and implements the parallel technique based on a graphics processing unit (GPU) for fast calculation. The details for the implementation of the proposed method are described, and it is tested using the geoid height undulation from the Earth Gravitational Model 2008 (EGM2008). The accuracy of the presented method is evaluated by comparing it with marine shipboard gravity data. Its efficiency is demonstrated through comparison with the conventional sequential method. The tests demonstrate that the proposed method can be employed for accurately calculating marine gravity anomalies and provides an advantage on computational efficiency.

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