Stephen A. Edwards Columbia University Crown
COMS W4995 001
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 10A-12P 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 Monads and IO
 pdf
Sun Sep 22 (no lecture; turn in homework)
 Homework 1 .hs filehw1.hs
Mon Sep 23 (Monads contd.)
Wed Sep 25
Mon Sep 30
Wed Oct 2 Using and Defining Modules
 pdf
Mon Oct 7 I/O
 pdf
Wed Oct 9 Functors
 pdf
Mon Oct 14 (Functors contd.)
Wed Oct 16 Monads
 pdf
Mon Oct 21 (Monads contd.)
Wed Oct 23 Lazy and Parallel Evaluation
 pdf
Mon Oct 28 Strategies
 pdf
Wed Oct 30 (Strategies contd.)
Mon Nov 4-5 Election Day Holiday
Wed Nov 6 Profiling
 pdf
Mon Nov 11 The Par Monad
 pdf
Wed Nov 13 The Haskell Tool Stack
 pdf
Mon Nov 18 Repa: Regular Parallel Arrays
 pdf
Wed Nov 20 Accelerate: GPU Arrays
 pdf
 Project Proposal
Mon Nov 25 The Lambda Calculus
 pdf
Wed Nov 27-29 Thanksgiving Holiday
Mon Dec 2 (Lambda contd.)
Wed Dec 4
Mon Dec 9
Wed Dec 18 Final Project Report and Presentations

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