We have dealt with this problem by using a more powerful hardware, the graphical processing units (GPUs).
This research parallelizes the batch self-organizing map by combining network partitioning and data partitioning methods with CUDA on the graphical processing unit to achieve significant training time reductions.
The most important of these is the Graphical Processing Unit (GPU).
One way to accelerate the FEM pipeline is by exploiting advances in modern computational hardware, such as the many-core streaming processors like the graphical processing unit (GPU).
An example of a SIMD-centric DSP is the graphical processing unit (GPU).
Recently, the graphical processing units (GPU), which is a device originally developed for rendering the graphical image, started to be used for scientific simulations.
Typically an HTML5/CSS3/JavaScript app will let the browser render CSS transitions before passing a series of calculations on to the graphical processing unit (GPU).
Additionally, new computational platforms such as general purpose programming on the graphical processing unit are being explored (Liepe et al., 2010).
Six years later, Nvidia released the GeForce 256, the first graphical processing unit (GPU) built specifically to produce 3D graphics for the burgeoning game industry.
The computation time of D-LBTE solvers might be further reduced in the future by implementation on graphical processing units and additional refinements.
