Class: COMP 605: Scientific Computing
Zack Humphries, Anuradha Agarwal, Thomas Keller
- main.c: serial code for the deep neural network
- evaluation.h: contains all the functions used in the serial code
- /openmp: contains all the files related to openMP
- /datasets: contains all the datasets used in this project
- /comparision: contains the forward propagation code used to compare results
- /CUDA: contains all the files related to CUDA
There are 3 command line arguments: number of inputs(30 or 4800), learning rate, and number of epochs
- How to compile:
gcc main.c -o main -lm
- How to run:
./main <numberofInputs> <learningRate> <numberofEpochs>
There is only one command line arguments: number of threads
- How to compile:
gcc -g -Wall main.c -o main -lm -fompenmp
- How to run:
./main <numberOfThreads>
There are no command line arguments
- How to compile:
nvcc main.cu -o main -lm
- How to run:
./main
https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data?resource=download
https://github.com/nihil21/parallel_nn
The file final_project_report.pdf
contains an in-depth analysis of the paralleled neural networks and algorithms.
MIT