Brittle Intergranular Failure in Grain Aggregates - A Computer Simulation by Means of the Graphics Processing Unit

2009 ◽  
Vol 409 ◽  
pp. 386-389
Author(s):  
Miriam Kupková ◽  
Samuel Kupka

Within a model considered, each of bonds between contacting grains is treated as a two-state system and represented by a binary variable. Its two values refer to the two possible states of bond – intact or broken. A Monte Carlo simulation of fracture is carried out on a set of binary variables arranged to a cubic lattice. The transition from one configuration of broken bonds to another is governed by a Griffith-like energy associated with each of configurations. The results demonstrate i) the capability of the model to provide a useful information (e.g. the increase in roughness of fracture surface with increasing temperature, that is the transition from “brittle” to “plastic” failure), and ii) the advantage of simulation by using the graphics processing unit (saving of a computational time).

Author(s):  
Eyad Hailat ◽  
Vincent Russo ◽  
Kamel Rushaidat ◽  
Jason Mick ◽  
Loren Schwiebert ◽  
...  

2012 ◽  
Vol 05 (02) ◽  
pp. 1250004 ◽  
Author(s):  
CHAO JIANG ◽  
HENG HE ◽  
PENGCHENG LI ◽  
QINGMING LUO

We present a graphics processing unit (GPU) cluster-based Monte Carlo simulation of photon transport in multi-layered tissues. The cluster is composed of multiple computing nodes in a local area network where each node is a personal computer equipped with one or several GPU(s) for parallel computing. In this study, the MPI (Message Passing Interface), the OpenMP (Open Multi-Processing) and the CUDA (Compute Unified Device Architecture) technologies are employed to develop the program. It is demonstrated that this designing runs roughly N times faster than that using single GPU when the GPUs within the cluster are of the same type, where N is the total number of the GPUs within the cluster.


2010 ◽  
Vol 18 (3-4) ◽  
pp. 193-201 ◽  
Author(s):  
Dennis C. Jespersen

The Computational Fluid Dynamics code OVERFLOW includes as one of its solver options an algorithm which is a fairly small piece of code but which accounts for a significant portion of the total computational time. This paper studies some of the issues in accelerating this piece of code by using a Graphics Processing Unit (GPU). The algorithm needs to be modified to be suitable for a GPU and attention needs to be given to 64-bit and 32-bit arithmetic. Interestingly, the work done for the GPU produced ideas for accelerating the CPU code and led to significant speedup on the CPU.


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