Application of GPGPU to Accelerate CFD Simulation

2018 ◽  
Author(s):  
Shafiul A. Mintu ◽  
David Molyneux
Keyword(s):  
Cfd Simulation ◽  
Cost Benefit Analysis ◽  
Academic Research ◽  
Cost Effective ◽  
Surface Flow ◽  
Massively Parallel ◽  
Processing Unit ◽  
Regular Wave ◽  
Central Processing ◽  
Simulation Time

Computational Fluid Dynamics (CFD) is widely used in industry and academic research to investigate complex fluid flow. The bottleneck of a realistic CFD simulation is its long simulation time. The simulation time is generally reduced by massively parallel Central Processing Unit (CPU) clusters, which are very expensive. In this paper, it is shown that the CFD simulation can be accelerated significantly by a novel hardware called General Purpose Computing on Graphical Processing Units (GPGPU). GPGPU is a cost-effective computing cluster, which uses the Compute Unified Device Architecture (CUDA) of NVIDIA devices to transform the GPU into a massively parallel processor. The paper demonstrates the faster computing ability of GPU compared to a traditional multi-core CPU. Two scenarios are simulated; one is a 2-dimensional simulation of regular wave and another one is a 3-dimensional motion of a floating ship on a regular wave. A smoothed particle hydrodynamics (SPH) based CFD solver is used for simulating the complex free-surface flow. The performance of a single GPU is compared against a commonly used 16 core CPU. For a large simulation of 6 degrees of freedom (DOF) ship motion simulation, the comparative study exhibits a speedup of more than an order of magnitude, reducing simulation time from 30 hours to about 2 hours. This indicates a CUDA enabled GPU card can be used as a cost-effective computing tool for a reliable and accurate SPH-based CFD simulation. The cost-benefit analysis of GPU over a CPU cluster is also discussed.

A Practical Method for the Rational Design of Ship Structures

1980 ◽  
Vol 24 (02) ◽  
pp. 101-113 ◽  
Author(s):  
Owen F. Hughes ◽  
Farrokh Mistree ◽  
Vedran Žanic
Keyword(s):  
Optimum Design ◽  
Rational Design ◽  
Cost Effective ◽  
Practical Method ◽  
General Purpose ◽  
Processing Unit ◽  
Ship Structures ◽  
Finite Element Program ◽  
Central Processing ◽  
Element Program

A practical, rationally based method is presented for the automated optimum design of ship structures. The method required the development of (a) a rapid, design-oriented finite-element program for the analysis of ship structures; (b) a comprehensive mathematical model for the evaluation of the capability of the structure; and (c) a cost-effective optimization algorithm for the solution of a large, highly constrained, nonlinear redesign problem. These developments have been incorporated into a program called SHIPOPT. The efficiency and robustness of the method is illustrated by using it to determine the optimum design of a complete cargo hold of a general-purpose cargo ship. The overall dimensions and the design loads are the same as those used in the design of the very successful SD14 series of ships. The redesign problem contains 94 variables, a nonlinear objective function, and over 500 constraints of which approximately half are non-linear. Program SHIPOPT required approximately eight minutes of central processing unit time on a CDC CYBER 171 to determine the optimum design.

Efficient parallelization of SPH algorithm on modern multi-core CPUs and massively parallel GPUs

2021 ◽  
pp. 2150054
Author(s):  
Pravin Jagtap ◽  
Rupesh Nasre ◽  
V. S. Sanapala ◽  
B. S. V. Patnaik
Keyword(s):  
High Performance ◽  
Performance Metrics ◽  
Computational Simulation ◽  
Massively Parallel ◽  
Benchmark Problems ◽  
Processing Unit ◽  
Central Processing ◽  
Neighbor Search ◽  
Computational Performance ◽  
Sph Algorithm

Smoothed Particle Hydrodynamics (SPH) is fast emerging as a practically useful computational simulation tool for a wide variety of engineering problems. SPH is also gaining popularity as the back bone for fast and realistic animations in graphics and video games. The Lagrangian and mesh-free nature of the method facilitates fast and accurate simulation of material deformation, interface capture, etc. Typically, particle-based methods would necessitate particle search and locate algorithms to be implemented efficiently, as continuous creation of neighbor particle lists is a computationally expensive step. Hence, it is advantageous to implement SPH, on modern multi-core platforms with the help of High-Performance Computing (HPC) tools. In this work, the computational performance of an SPH algorithm is assessed on multi-core Central Processing Unit (CPU) as well as massively parallel General Purpose Graphical Processing Units (GP-GPU). Parallelizing SPH faces several challenges such as, scalability of the neighbor search process, force calculations, minimizing thread divergence, achieving coalesced memory access patterns, balancing workload, ensuring optimum use of computational resources, etc. While addressing some of these challenges, detailed analysis of performance metrics such as speedup, global load efficiency, global store efficiency, warp execution efficiency, occupancy, etc. is evaluated. The OpenMP and Compute Unified Device Architecture[Formula: see text] parallel programming models have been used for parallel computing on Intel Xeon[Formula: see text] E5-[Formula: see text] multi-core CPU and NVIDIA Quadro M[Formula: see text] and NVIDIA Tesla p[Formula: see text] massively parallel GPU architectures. Standard benchmark problems from the Computational Fluid Dynamics (CFD) literature are chosen for the validation. The key concern of how to identify a suitable architecture for mesh-less methods which essentially require heavy workload of neighbor search and evaluation of local force fields from neighbor interactions is addressed.

scholarly journals A Cost-Effective Solution to Improving the Electrical Performance of Metal Contacting Interfaces in IC System under Temperature-Humidity Environment

2019 ◽  
Vol 9 (19) ◽  
pp. 3950
Author(s):  
Li ◽  
Meng ◽  
Shi ◽  
Gao ◽  
Zhang ◽  
...  
Keyword(s):  
Contact Resistance ◽  
Integrated Circuit ◽  
System Reliability ◽  
Cost Effective ◽  
Electrical Performance ◽  
Processing Unit ◽  
Test Results ◽  
Effective Solution ◽  
Central Processing ◽  
Humidity Environment

Temperature-humidity (TH) induced failure mechanism (FM) of metal contacting interfaces in integrated circuit (IC) systems has played a significant role in system reliability issues. This paper focuses on central processing unit (CPU)/motherboard interfaces and studies several factors that are believed to have a great impact on TH performance. They include: Enabling load, surface finish quality, and contacting area. Test vehicles (TVs) of Clarkdale package and of Ibex peak motherboard were designed to measure low level contact resistance (LLCR) for catching any failure. Several sets of design of experiments (DOE) were conducted on 85°C/85% relative humidity and test results were analyzed. A proposal that correlates asperity spots and contact tip design with contact resistance was proposed and thus a cost-effective solution for improving electrical performance under TH was deduced. The proposal has proven to be reasonably effective in practice.

scholarly journals Two-Vehicle Coordination System for Omnidirectional Transportation Based on Image Processing and Deviation Prediction

2021 ◽  
Author(s):  
Huajun Song ◽  
Yanqi Wu ◽  
Yuxing Wu ◽  
Guangbing Zhou ◽  
Chunbo Luo
Keyword(s):  
Image Processing ◽  
Manufacturing Industry ◽  
Cost Effective ◽  
Image Sensor ◽  
Mobile Platform ◽  
Normal Operation ◽  
Processing Unit ◽  
Mobile Platforms ◽  
Central Processing ◽  
Omnidirectional Mobile Platform

AbstractOmnidirectional mobile platform is essential due to its excellent mobility and versatility. With the development of the manufacturing industry, how to transport oversized or overweight goods has become a new problem. Compared with manufacturing omnidirectional mobile platforms with different specifications, it is more cost-effective and flexible to coordinate two non-physically connected omnidirectional platforms to transport overweight and oversized cargo. The roughness of the actual deployment environment and the mechanical deflection between the two vehicles have a significant impact on the normal operation of the system. This paper combines mechanical wheels, image processing algorithms and collaboration algorithms to create a novel and practical split-type omnidirectional mobile platform based on image deviation prediction for transporting oversized or overweighted goods. The proposed system collects raw measurements from a distance sensor and an image sensor, transmits them to a central processing unit through a wireless communication module and calculates and predicts the relative deflection between the two vehicles based on our derived mathematical model. This information is then fed to a Kalman filter and PID control algorithm to coordinate the two vehicles. The effectiveness and performance of our system have been thoroughly tested, which has already been applied in a bullet train production line.

Computational Fluid Dynamics Assisted Control System Design With Applications to Central Processing Unit Chip Cooling

2013 ◽  
Vol 5 (4) ◽  
Author(s):  
R. Zhang ◽  
C. Zhang ◽  
J. Jiang
Keyword(s):  
Fluid Dynamics ◽  
Control System ◽  
Control Systems ◽  
Design Methodology ◽  
Cfd Simulation ◽  
Cooling System ◽  
Control System Design ◽  
Processing Unit ◽  
Central Processing ◽  
Chip Cooling

In this paper, a computational fluid dynamics (CFD) assisted control system design methodology has been described in detail. The entire design and evaluation procedure has been illustrated through a feedback control system synthesis for a central processing unit (CPU) chip cooling system. The design methodology starts with a full-scale CFD simulation of the nonlinear dynamic process to generate the input and output databases of the process. Using this data set, linear dynamic models around specified operating points are obtained using system identification techniques. Based on these models, one can design appropriate control systems to meet the required closed-loop control system specifications. To illustrate the effectiveness of this technique, it has been used to design a controller for a PC chip cooling system. In particular, the coupling issues between ‘real-time’ dynamic controllers with non real-time CFD simulation have been resolved. A physical experimental test bench based on a cooling system of a Pentium III CPU has been constructed. The feedback linear control systems designed by the proposed CFD approach have been evaluated experimentally for six CPU load conditions.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

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