Large-Scale Parallel Multibody Dynamics With Frictional Contact on the GPU

2008 ◽  
Author(s):  
Dan Negrut ◽  
Alessandro Tasora ◽  
Mihai Anitescu
Keyword(s):  
Large Scale ◽  
Frictional Contact ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Rigid Bodies ◽  
Contact Dynamics ◽  
Iteration Algorithm ◽  
Symmetric Convex ◽  
Processing Unit ◽  
Multiple Data

In the context of simulating the frictional contact dynamics of large systems of rigid bodies, this paper reviews a novel method for solving large cone complementarity problems by means of a fixed-point iteration algorithm. The method is an extension of the Gauss-Seidel and Gauss-Jacobi methods with overrelaxation for symmetric convex linear complementarity problems. Convergent under fairly standard assumptions, the method is implemented in a parallel framework by using a single instruction multiple data (SIMD) computation paradigm promoted by the Compute Unified Device Architecture (CUDA) library for graphical processing unit (GPU) programming. The framework is anticipated to become a viable tool for investigating the dynamics of complex systems such as ground vehicles running on sand, powder composites, and granular material flow.

A GPU-Based Implementation of a Cone Convex Complementarity Approach for Simulating Rigid Body Dynamics With Frictional Contact

2008 ◽  
Author(s):  
Alessandro Tasora ◽  
Dan Negrut ◽  
Mihai Anitescu
Keyword(s):  
Frictional Contact ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Rigid Bodies ◽  
Rigid Body Dynamics ◽  
Contact Dynamics ◽  
Iteration Algorithm ◽  
Symmetric Convex ◽  
Processing Unit ◽  
Multiple Data

In the context of simulating the frictional contact dynamics of large systems of rigid bodies, this paper reviews a novel method for solving large cone complementarity problems by means of a fixed-point iteration algorithm. The method is an extension of the Gauss-Seidel and Gauss-Jacobi methods with over-relaxation for symmetric convex linear complementarity problems. Convergent under fairly standard assumptions, the method is implemented in a parallel framework by using a single instruction multiple data computation paradigm promoted by the Compute Unified Device Architecture library for graphical processing unit programming. The framework supports the analysis of problems with a large number of rigid bodies in contact. Simulation thus becomes a viable tool for investigating the dynamics of complex systems such as ground vehicles running on sand, powder composites, and granular material flow.

scholarly journals Predictor-Corrector Interior-Point Algorithm for the General Linear Complementarity Problem

2021 ◽  
Vol 15 (1) ◽  
pp. 11-14
Author(s):  
Zsolt Darvay ◽  
Ágnes Füstös
Keyword(s):  
Interior Point ◽  
Large Scale ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Point Of View ◽  
General Linear ◽  
Interior Point Algorithm ◽  
Step Size ◽  
Predictor Corrector

Abstract We study a predictor-corrector interior-point algorithm for solving general linear complementarity problems from the implementation point of view. We analyze the method proposed by Illés, Nagy and Terlaky [1] that extends the algorithm published by Potra and Liu [2] to general linear complementarity problems. A new method for determining the step size of the corrector direction is presented. Using the code implemented in the C++ programming language, we can solve large-scale problems based on sufficient matrices.

scholarly journals Enabling Large-Scale Simulations of Quantum Transport with Manycore Computing

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 253
Author(s):  
Yosang Jeong ◽  
Hoon Ryu
Keyword(s):  
Quantum Transport ◽  
Large Scale ◽  
Performance Enhancement ◽  
Silicon Nanowire ◽  
Matrix Multiplication ◽  
Tight Binding ◽  
Optimization Techniques ◽  
Wide Energy Range ◽  
Processing Unit ◽  
Binding Model

The non-equilibrium Green’s function (NEGF) is being utilized in the field of nanoscience to predict transport behaviors of electronic devices. This work explores how much performance improvement can be driven for quantum transport simulations with the aid of manycore computing, where the core numerical operation involves a recursive process of matrix multiplication. Major techniques adopted for performance enhancement are data restructuring, matrix tiling, thread scheduling, and offload computing, and we present technical details on how they are applied to optimize the performance of simulations in computing hardware, including Intel Xeon Phi Knights Landing (KNL) systems and NVIDIA general purpose graphic processing unit (GPU) devices. With a target structure of a silicon nanowire that consists of 100,000 atoms and is described with an atomistic tight-binding model, the effects of optimization techniques on the performance of simulations are rigorously tested in a KNL node equipped with two Quadro GV100 GPU devices, and we observe that computation is accelerated by a factor of up to ∼20 against the unoptimized case. The feasibility of handling large-scale workloads in a huge computing environment is also examined with nanowire simulations in a wide energy range, where good scalability is procured up to 2048 KNL nodes.

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