High Performance Computing Cluster Nodes:

GPU Computing: GPU computing is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing. The model for GPU computing is to use a CPU and GPU together in a heterogeneous computing model. The sequential part of the application runs on the CPU and the computationally-intensive part runs on the GPU. From the user’s perspective, the application just runs faster because it is using the high-performance of the GPU to boost performance.

Learn more about nVidia Tesla computing solution:  http://www.nvidia.com/object/tesla_computing_solutions.html

The application developer has to modify their application to take the compute-intensive kernels and map them to the GPU. The rest of the application remains on the CPU. Mapping a function to the GPU involves rewriting the function to expose the parallelism in the function and adding “C” keywords to move data to and from the GPU.

GPU computing is enabled by the massively parallel architecture of NVIDIA’s GPUs called the CUDA architecture. The CUDA architecture consists of 100s of processor cores that operate together to crunch through the data set in the application.

Learn more about nVidia CUDA technology: http://www.nvidia/cuda