Abstract
Image classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation.
Highlights
In recent years, parallel computing and soft computing has become a rapidly evolving field of study
The benchmark image classification algorithm used in this project is Convolutional Neural Network
Researchers say that the Graphics Processing Unit (GPU) can gain speedup up to 60% more than that of CPUs
Summary
Parallel computing and soft computing has become a rapidly evolving field of study. It is basically an Application Programming Interface for shared Memory Model programming. Python has its separate parallel processing module named Multiprocessing. The main drawback of Python’s Multiprocessing module is that it cannot be used for handling large numeric data. It cannot be used in Deep Learning Frameworks such as Keras as it decreases the Revised Manuscript Received on April 25, 2020. Python has a Parallel and Distributed computing framework called Ray. Ray can be used for developing emerging AI applications such as image classification, face recognition etc. The system is configured with 16 GB RAM with 4 CPU Cores and Tesla P100 GPU This project compares the performance of 2-core, 3-core and 4-core parallelized CPUs with GPU
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