Parallel Functional Programming
Fall 2026
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| COMS W 4995 001 Parallel Functional Programming Fall 2026 |
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Class meets Mondays, Wednesdays 1:10 - 2:25 PM in 451 CSB.
| Name | Office hours | Location | |
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| Prof. Stephen A. Edwards | sedwards@cs.columbia.edu | by appointment | |
| Jonathan Yang Chen | jyc2183@columbia.edu | tba |
Prerequisites: COMS 3157 Advanced Programming or the equivalent. Knowledge of at least one programming language and related development tools/environments required. Functional programming experience not required.
Functional programming in Haskell, with an emphasis on parallel programs.
The goal of this class is to introduce you to the functional programming paradigm. You will learn to code in Haskell; this experience will also prepare you to code in other functional languages. The first half the the class will cover basic (single-threaded) functional programming; the second half will cover how to code parallel programs in a functional setting.
| Date | Lecture | Notes | Due |
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| Wed Sep 9 | Introduction Basic Haskell |
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| Mon Sep 14 | (Basics contd.) |
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| Wed Sep 16 | Types and Pattern Matching |
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| Sun Sep 20 |
Homework 1
.hs file
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| Mon Sep 21 | (Types contd.) |
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| Wed Sep 23 | (Types contd.) |
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| Mon Sep 28 | Monads and IO |
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| Wed Sep 30 | (Monads contd.) |
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| Mon Oct 5 | (Monads contd.) |
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| Wed Oct 7 | (Monads contd.) |
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| Sun Oct 11 |
Homework 2
.hs file
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| Mon Oct 12 | Lazy Evaluation and Seq |
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| Wed Oct 14 | Lazy Evaluation and Seq (contd.) |
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| Sun Oct 18 | Project Proposal | ||
| Mon Oct 19 | (no lecture) |
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| Wed Oct 21 | (no lecture) |
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| Sun Oct 25 |
Homework 3
.zip file
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| Mon Oct 26 | (no lecture) |
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| Wed Oct 28 | Strategies |
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| Sun Nov 1 |
Homework 4
.pdf file
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| Mon Nov 2-3 | Election Day Holiday | ||
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| Mon Nov 9 | The Par Monad |
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| Wed Nov 11 | The Haskell Tool Stack Repa: Regular Parallel Arrays |
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| Mon Nov 16 | Accelerate: GPU Arrays |
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| Wed Nov 18 | The Lambda Calculus |
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| Mon Nov 23 | (Lambda contd.) |
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| Wed Nov 25-27 | Thanksgiving Holiday | ||
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| Mon Dec 7 | |||
| Wed Dec 9 | |||
| Mon Dec 14 | |||
| Tue Dec 22 | Final Project Exams |
The project should be a parallel implementation of some algorithm/technique in Haskell. Marlow parallelizes a Sudoku solver and a K-means clustering algorithm in his book; these are baseline projects. I am looking for something more sophisticated than these, but not dramatically more complicated.
Do the project in groups of 2 or 3. List all your names and UNIs in the proposal and final report
Strive for a little well-written, well-tested program that handles everything gracefully rather than a large, feature-filled system. If you're short on time, drop a feature in preference to improving the code you have.
Other project ideas include any sort of map/reduce application, graphics rendering, physical simulation (e.g., particles), parallel grep or word count, a Boolean satisfiability solver, or your favorite NP-complete problem. If your program is algorithmically simple (e.g., word count or word frequency count), it need to scale to huge inputs. AI (as opposed to machine learning) applications, such as game playing algorithms, are generally a good idea. Algorithms that have a lot of matrix multiplication at their core (e.g., deep learning) are less suitable.
Feel free to ask the instructor or TAs for project advice or criticism
See the N-queens repository and report as an example of the sort of project we are looking for. That is, start with a simple sequential implementation of an existing algorithm, improve its speed and memory footprint in a sequential setting, then parallelize it and improve it further. A good metric to optimize is how much your implementation speeds up when given, say, 8 cores compared with 1. You can use these speedup numbers to estimate the "parallel fraction" suggested by Amdahl's law, another interesting metric.