Publication Date
4-2022
School
School of Business
Major
Computer Science
Keywords
GPU, deep learning, GPGPU, convolutional, neural network, accelerate, parallelism, multithread
Disciplines
Business | Computer Sciences
Recommended Citation
Helmick, Conor, "General Purpose Computing on Graphics Processing Units for Accelerated Deep Learning in Neural Networks" (2022). Senior Honors Theses. 1171.
https://digitalcommons.liberty.edu/honors/1171
Abstract
Graphics processing units (GPUs) contain a significant number of cores relative to central processing units (CPUs), allowing them to handle high levels of parallelization in multithreading. A general-purpose GPU (GPGPU) is a GPU that has its threads and memory repurposed on a software level to leverage the multithreading made possible by the GPU’s hardware, and thus is an extremely strong platform for intense computing – there is no hardware difference between GPUs and GPGPUs. Deep learning is one such example of intense computing that is best implemented on a GPGPU, as its hardware structure of a grid of blocks, each containing processing threads, can handle the immense number of necessary calculations in parallel. A convolutional neural network (CNN) created for financial data analysis shows this advantage in the runtime of the training and testing of a neural network.
