This repository contains code written and utilized during a graduate course in High-Performance scientific computing at the University of Washington in Spring of 2016. The course was hosted on github and code was written in Python/C and utilized open source tools such as OpenMP and MPI to implement optimized parallel computational routines. The purpose of this repository is to provide a small showcase of my ability to write code in the Python and C languages.
Within each assignment'_' folder the README.md document contains the original rubric of the assignment with a detailed description of the files and expectations.
In addition to writing code, each assignment'_' folder contains a report folder with a writeup on some of the results along with particular visualizations and answers to various questions.
- Assignment 1 - Wrote code to implement and visualize the Collatz conjecture, perform gradient descent, and iteratively solve matrix equations via the Jacobi/Gauss-Seidel methods.
- Assignment 2 - Implemented common linear algebra routines in C.
- Assignment 3 - Used OpenMP to numerically calculate integrals using the trapezoidal/simpson rule algorithms in parallel.
- Assignment 4 - Wrote code to solve the heat-equation with a MPI methodology.