Year: 2018
Type: article
Abstract: GPGPU or General-Purpose Computing on Graphics Programming Unit, also called as Heterogeneous Computing, is rapidly emerging into an area of great interest to computer scientists and engineers.A single or a small group of connected GPUs can solve certain classes of computational problems faster than a multicore CPU can.GPUs can effectively and efficiently solve problems that have a high level of parallelization in them.To achieve this sort of GPU programmability, a C-based framework called CUDA exists.CUDA (Compute Unified Device Architecture) is a framework created and maintained by NVIDIA to help simplify the task of GPU programming.This paper presents the foundations of this computational model that can be harnessed for solving complex computational problems, while also mentioning its limitations.
Author: Siddhante Nangla
Institution: University of Mumbai
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2808569185