Graduation Year

2016

Date of Submission

4-2016

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Mathematics

Reader 1

Arthur H. Lee

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Terms of Use for work posted in Scholarship@Claremont.

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© 2016 Manav S. Kohli

Abstract

With diminishing gains in processing power from successive generations of hardware development, there is a new focus on using advances in computer science and parallel programming to build faster, more efficient software. As computers trend toward including multiple and multicore processors, parallel computing serves as a promising option for optimizing the next generation of software applications. However, models for implementing parallel programs remain highly opaque due to their reliance on languages such as Fortran, C, and C++. In this paper I investigate Python an option for implementing parallel programming techniques in application development. I analyze the efficiency and accessibility of MPI for Python and IPython Parallel packages by calculating in parallel using a Monte Carlo simulation and comparing their speeds to the sequential calculation. While MPI for Python offers the core functionality of MPI and C-like syntax in Python, IPython Parallel's architecture provides a truly unique model.

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