GPU Application in Cuda Memory

Abstract

Nowadays modern computer GPU (Graphic Processing Unit) became widely used to improve the performance of a computer, which is basically for the GPU graphics calculations, are now used not only for the purposes of calculating the graphics but also for other application. In addition, Graphics Processing Unit (GPU) has high computation and low price. This device can be treat as an array of SIMD processor using CUDA software. This paper talks about GPU application, CUDA memory and efficient CUDA memory using Reduction kernel. High-performance GPU application requires reuse of data inside the streaming multiprocessor (SM). The reason is that onboard global memory is simply not fast enough to meet the needs of all the streaming multiprocessor on the GPU. In addition, CUDA exposes the memory space within the SM and provides configurable caches to give the developer the greatest opportunity of data reuse.

Serial and parallel implementation of Needleman-Wunsch algorithm

“…CUDA allows a host to access memories residing in a device memory [24]. Each thread is assigned with a local memory called a register, and all threads in all blocks have access to a global memory to enable block-level communication [23]. Other memory types are shared memory, texture memory, and constant memory; however, these are out of scope in this research.…”

“…Compute Unified Device Architecture (CUDA) is one recently introduced framework that makes use of parallel compute engines in NVIDIA GPUs to solve complex computational problems efficiently [23]. CUDA is mapped to various applications and enhances the performance significantly.…”