Tag: numerical modeling
In this paper we describe algorithms for the massively parallel evaluation and differentiation of polynomials in several variables
We consider the use of commodity graphics processing units (GPUs) for the common task of numerically integrating ordinary differential equations (ODEs), achieving speedups of up to 115-fold over comparable serial CPU implementations, and 15-fold over multithreaded CPU code with SIMD intrinsics.
Application of Graphics Processing Units to the Study of Non-linear Dynamics of the Exciton Bose-Einstein Condensate
We have investigated the application of GPUs using NVIDIA’s CUDA programming environment to the numerical solution of the Gross-Pitaevskii equation, which describes the dynamics of the Bose-Einstein condensate of excitons in a semiconductor quantum well.
Implementation of GEOS-5 on GPUs provides a useful benchmark for the scalability of global atmospheric models on GPUs, and facilitates evaluation of future system architecture configurations.
In the current paper, we described the implementation of a numerical solver for simulating chemically reacting flow on the GPU. The fluid dynamics is modeled using high-order shock-capturing schemes, and the chemical kinetics is solved using an implicit solver.
Aspects of Numerical Algorithms, Parallelization and Applications have been a major thrust of research and have application throughout computational science and engineering. Numerical algorithms are widely used by scientists engaged in various scientific areas.
A computational code for the numerical integration of the incompressible Navier-Stokes equations for the execution of accurate calculations with the approach of the Direct Numerical Simulation (DNS), is implemented on a specially-assembled hybrid CPU/GPU computing system.
The CUBLAS and CULA acceleration of adaptive finite element framework for bioluminescence tomography
In this paper, we for the first time introduce a new kind of acceleration technology to accelerate the AFE framework for BLT, using the graphics processing unit (GPU). Besides the processing speed, the GPU technology can get a balance between the cost and performance.
In this work, authors used OpenCL framework to accelerate an EMRI modeling application using the hardware accelerators – Cell BE and Tesla CUDA GPU. Authors describe these compute technologies, present performance results, and then compare them with those from our previous implementations based on the native CUDA and Cell SDKs.