Stephen A. Edwards Columbia University Crown
COMS W4995 003
Parallel Functional Programming
Fall 2020

General Information

Class meets Mondays, Wednesdays 5:40 - 6:55 PM in Online; see Courseworks.

Staff

Name Email Office hours Location
Prof. Stephen A. Edwards sedwards@cs.columbia.edu F 2-4 Online
Shravan Karthik sk4653@columbia.edu Th 2-4 Online
Benjamin Flin brf2117@columbia.edu W 7-9 Online
Garrison Grogan gg2652@columbia.edu F 5-7 Online

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 9 Introduction
Basic Haskell
pdf
pdf
Mon Sep 14 Types and Typeclasses
pdf
Wed Sep 16 Basic Function Definitions
pdf
Fri Sep 18 (no lecture; turn in homework)
Homework 1 .hs filehw1.hs
Mon Sep 21 Recursion and Higher Order Functions
pdf
Wed Sep 23 (Recursion contd.)
Mon Sep 28 Using and Defining Modules
pdf
Wed Sep 30 User-Defined Types
pdf
Fri Oct 2 (no lecture; turn in homework)
Homework 2 .hs filehw2.hs
Mon Oct 5 (User-Defined Types contd.)
Wed Oct 7 (User-Defined Types contd.)
Mon Oct 12 I/O
pdf
Wed Oct 14 Functors
pdf
Sun Oct 18 (no lecture; turn in homework)
Homework 3 .hs filehw3.hs
Mon Oct 19 (Functors contd.)
Wed Oct 21 Monads
pdf
Mon Oct 26
Wed Oct 28
Fri Oct 30 Homework 4 .zip filehw4.zip
Mon Nov 2 Election Day Holiday
Wed Nov 4
Mon Nov 9
Wed Nov 11
Mon Nov 16 Lazy and Parallel Evaluation
pdf
Wed Nov 18
Mon Nov 23
Wed Nov 25 Thanksgiving Holiday
Mon Nov 30
Wed Dec 2
Mon Dec 7 The Par Monad
pdf
Wed Dec 9
Mon Dec 14

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 alone or in pairs. 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

Class Policies

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