CMSC 12300: Computer Science with Applications III

The University of Chicago, Spring 2022

Syllabus

Course Staff

Instructor: Matthew Wachs
Email: mwachs

TA: William Wang

Topics

This course is about Big Data: the challenges of working with it, and the solutions that have ben developed to successfully overcome them. Topics include:

• Algorithms:
  • considerations and changes needed when moving from smaller data sets to large ones
  • analysis of computational time and memory requirements and how they scale with data size
  • methods and conceptual frameworks for dividing up the work of an algorithm into separate tasks that can be run in parallel on multiple computing resources
• C: an expansion of your programming skills to include, in your repertoire, arguably the most-widely used language in the world, a lingua franca for computer scientists, a low-level language that offers higher performance than interpreted languages such as Python
• Big Data and cloud computing environments and programming paradigms:
  • MapReduce
  • Hadoop
  • Multi-process and multi-threaded programming
  • Concurrency and synchronization primitives (mutexes, condition variables)
  • MPI
  • Spark

Goals

By the end of the course, you should be able to perform analyses on data sets that are too large to fit on a single computer, and complete computations that would take impracticallly long if they were to be performed on a single computer. You will know several approaches for splitting up work, and their respective advantages and disadvantages, thus being able to choose a suitable one for a particular task. You will have used these tools in instructive examples and optionally in your own project, and will have had the opportunity to receive extensive guidance on how to approach your project. You will have gained basic familiarity with the C programming language, a language widely used by computer scientists and programmers, and be familiar with its basic data structures and memory management philosophy. You will be familiar with the kinds of pernicious errors that arise in, and that are unique to, concurrent code, and how to address them.

Course Components

The course consists of:

• Lectures
• Labs: give you the opportunity to practice with real Big Data environments and get immediate feedback and assistance from course staff (not graded)
• Programming assignments: give you more in-depth practice on selected material than would be possible in the labs; cumulatively, the 3-4 programming assignments contribute 50% towards your final grade
• Project: an optional extended, open-ended team project, similar to the project in CS 122; you will propose, check in with me, and give a final presentation alongside submitting your code. The theme of the project is answering hypotheses on large data sets. Projects will count for the balance of your final grade
• Exams: for students not completing a project, two programming exams confirm your mastery of the skills from the course; cumulatively, they contribute 50% towards your final grade; we will discuss in class the considerations involved in choosing between projects and exams

Academic Honesty

The University's rules on academic honesty apply equally to this course as they did in the prior courses in the sequence and will be rigorously and rigidly enforced. Unless an assignment explicitly permits teamwork, you must not use code from any other student, show your code to any other student, or incorporate any code other than the code you have yourself written, except where you are using brief snippets of code from cited sources. If you discuss approaches with other students, then the level of detail and collaboration must not be such that your submissions will be close to identical. If you have any doubts, questions, or concerns, please ask, particularly in advance.

Textbook

This course does not have a textbook. However, this book is highly relevant to the course and is available online at no cost; you may find it of value.

Lecture Policies

You may not make audio or video recordings of lectures or take pictures of slides or the blackboard. Code from class will be posted to the course web site. Students unable to attend a lecture due to, for instance, quarantine or isolation, should contact the instructor in advance, and we will make arrangements to keep you up to date on lecture material.

Regrade Requests

Graders, TAs, and instructors do make mistakes, so it is normal and expected that a few assignments will have grading errors. In order to manage the workload of regrade requests, however, regrade requests must be submitted within one week of the assignment being returned. No regrade requests will be accepted after this point.