We present an implementation of the numerical modeling of elastic waves propagation, in 2D anisotropic materials, using the new parallel computing devices (PCDs)
The goal was to find out whether Geant4 physics simulations could benefit from GPU acceleration and how difficult it is to modify Geant4 code to run in a GPU
This paper presents a comparison of OpenMP and OpenCL based on the parallel implementation of algorithms from various fields of computer applications
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms.…
We demonstrate that the most expensive kernel in the model executes more than three times faster on the GPU than the CPU. These improvements are expected to provide improved efficiency when incorporated into the full model that has been configured for the target problem
A comparison of the FDTD algorithm implemented on an integrated GPU versus a GPU configured as a co-processor
The FDTD method is implemented on the Accelerated Processing Unit’s integrated GPU using the DirectCompute application programming interface and compared against an FDTD implementation on a GPU configured as a co-processor via a PCIe bus.
Numerical Solutions of Heat and Mass Transfer in Capillary Porous Media Using Programmable Graphics Hardware
We have presented our numerical approximations to the solution of the heat and mass transfer equation with the second kind of boundary and initial conditions using finite difference method on GPGPUs.
We present a comparison of several modern C++ libraries providing high-level interfaces for programming multi- and many-core architectures on top of CUDA or OpenCL