Stephen A. Edwards Columbia University Crown
COMS W4995 003
Parallel Functional Programming
Fall 2024

General Information

Class meets Mondays, Wednesdays 1:10 - 2:25 PM in 633 Mudd.

Staff

Name Email Office hours Location
Prof. Stephen A. Edwards sedwards@cs.columbia.edu by appointment
Sparsh Binjrajka sb4835@columbia.edu Th 2-4 DSI Lounge (4th Fl. Mudd)
Leo (Feitong) Qiao flq2101@columbia.edu F 2-4P Zoom

Overview

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.

Schedule

Date Lecture Notes Due
Wed Sep 4 Introduction
Basic Haskell
 pdf
pdf
Mon Sep 9 (Basics contd.)
Wed Sep 11 Types and Pattern Matching
 pdf
Mon Sep 16 (Types contd.)
Wed Sep 18 (Types contd.)
Sun Sep 22 (no lecture; turn in homework)
 Homework 1 .hs filehw1.hs
Mon Sep 23 Monads and IO
 pdf
Wed Sep 25 (Monads contd.)
Mon Sep 30 (Monads contd.)
Wed Oct 2 (Monads contd.)
Sun Oct 6 (no lecture; turn in homework)
 Homework 2 .hs filehw2.hs
Mon Oct 7 Lazy Evaluation and Seq
 pdf
Tue Oct 8 Using and Defining Modules
I/O
 pdf
pdf
Wed Oct 9 Lazy Evaluation and Seq (contd.)
Thu Oct 10 Parallel Evaluation
 pdf
Mon Oct 14 (no lecture)
Wed Oct 16 (no lecture)
Sun Oct 20 (no lecture; turn in homework)
 Homework 3 .zip filehw3.zip
Mon Oct 21 (no lecture)
Wed Oct 23 Strategies
 pdf
Mon Oct 28 (Strategies contd.)
Sun Nov 3 (no lecture; turn in homework)
 Homework 4 .pdf filehw4.pdf
Mon Nov 4-5 Election Day Holiday
Wed Nov 6 The Haskell Tool Stack
 pdf
Mon Nov 11 Repa: Regular Parallel Arrays
 pdf
Wed Nov 13 Accelerate: GPU Arrays
 pdf
Mon Nov 18 The Lambda Calculus
 pdf
Wed Nov 20 (Lambda contd.)
 Project Proposal
Mon Nov 25
Wed Nov 27-29 Thanksgiving Holiday
Mon Dec 2
Wed Dec 4
Mon Dec 9
Wed Dec 18 Final Project Report and Presentations

Teams

:

Mooizz Abdul, Adele Bai, Viktor Basharkevich, Samhit Chowdary Bhogavalli, Roberto Brera, Linh Bui, Caiwu Chen, Riz Chen, Nick Ching, Erica Choi, Claudia Cortell, Letong Dai, Kevin Durand, Kyle Edwards, Tony Giannini, Haolin Guo, Max Hahn, Nicholas Hersh, Noam Hirschorn, Nikolaus Holzer, Daniel Ivanovich, Timothy Johns, Jittisa Kraprayoon, Sindhu Krishnamurthy, Nuneke Kwetey, Yixuan LI, Yuanqing Lei, Jiaqian Li, Luana Liao, Henry Lin, Keith Lo, Catherine Lyu, Daniel Manjarrez, Andre Mao, George Morgulis, Vincent Mutolo, Jonathan Nalikka, Dorothy Nelson, Ava Penn, Pavan Ravindra, Mohsin Rizvi, Matthew Rosenberg, Milin Saini, Avighna Suresh, Samyukkta Suryanarayanan, Jonathan Tavarez, Hongcheng Tian, Minh Hien Tran, Isabel Tu, Phoebe Wang, Ardrian Wong, Phillip Yan, Max Zhang, Sean Zhang, and Zi Zhuang

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The Project

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

There are three deliverables:

  1. A one- or two-page proposal that gives the TAs and me an inda of what you plan to do so we can give you feedback about restricting or increasing the scope. Upload a PDF file to Courseworks describing your project and team members, if any.
  2. A report describing your project: what you implemented and how, some performance figures indicating how much better your solution runs in parallel (e.g., time its execution on one core and compare that to running it on multiple cores), and a full listing of the code you wrote. Upload a multi-page PDF file to Courseworks; due during Finals Week.
  3. Along with your report, submit a .tar.gz file including the code and test cases for your project. Make it so I can compile and run it, perhaps by including a README file with instructions for running it with the Haskell Stack. Due with the repot.

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

Resources

Links