Zombie Panda Breeders

From CDOT Wiki
Jump to: navigation, search

Welcome to the team page for Zombie Panda Breeders.

We are a team focused on parallelizing C/C++ applications using CUDA for the Parallel Programming course (GPU610/DPS915) at Seneca for the Fall 2012 semester.

The team consists of:

Andrei Kopytov

Zack Bloom

Assignment 1

RSA Key Generator

Attempted by: Andrei Kopytov

This is an open source project hosted on code.google.com. The source was written from scratch in C++ to implement RSA encryption.

It currently supports prime number generation, key generation and encryption/decryption of files or strings.

The project can be found here: http://code.google.com/p/rsa/

My goal for this project will be to parallelize the longMultiply() function inside the BigInt class.

/* Multiplies two unsigned char[] the long way. */
void BigInt::longMultiply(unsigned char *a, unsigned long int na, unsigned char *b, unsigned long int nb, unsigned char *result)
	std::fill_n(result, na + nb, 0);
	unsigned char mult(0);
	int carry(0);
	for (unsigned long int i(0L); i < na; i++)
		for (unsigned long int j(0L); j < nb; j++)
			mult = a[i] * b[j] + result[i + j] + carry;
			result[i + j] = static_cast<int>(mult) % 10;
			carry = static_cast<int>(mult) / 10;
		if (carry)
			result[i + nb] += carry;
			carry = 0;

Mutation Simulator

Attempted by: Zack Bloom

This is a project I had made on my own. It is a simple mutation simulator.

This type of project gets used in genetic algorithms, and can be considered a primitive engine of one.

The goal of a genetic algorithm is to find an observable optimum solution within a population by breeding over many generations.

The main difference between my Mutation Simulator and a genetic algorithm, is that my mutation simulator lacks a metric to judge the fitness of each offspring within a population.

The general idea of how this works is with this diagram (minor differences can be found within my program)


These are the two methods I was planning to parallelize

void Offspring::mutate(float mutationChance)
	for (int i = 0; i < amountOfGenes; i++){


void Population::breed(int index, float mutationChance)
	int aOrB;
	for (int i = 0; i < organisms[index]->amountOfGenes; i++)
		aOrB = rand() % 2;
		if (aOrB == 0)//
			organisms[index+1]->geneticMakeup[i] = organisms[index]->geneticMakeup[i];
		else //
			organisms[index]->geneticMakeup[i] = organisms[index+1]->geneticMakeup[i];