COMS W4995 003 Parallel Functional Programming Fall 2024

General Information

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

Staff

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
Mon Sep 9	(Basics contd.)
Wed Sep 11	Types and Pattern Matching
Mon Sep 16	(Types contd.)
Wed Sep 18	(Types contd.)
Sun Sep 22	(no lecture; turn in homework)		Homework 1 .hs file
Mon Sep 23	Monads and IO
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 file
Mon Oct 7	Lazy Evaluation and Seq
Tue Oct 8	Using and Defining Modules I/O
Wed Oct 9	Lazy Evaluation and Seq (contd.)
Thu Oct 10	Parallel Evaluation
Mon Oct 14	(no lecture)
Wed Oct 16	(no lecture)
Sun Oct 20	(no lecture; turn in homework)		Homework 3 .zip file
Mon Oct 21	(no lecture)
Wed Oct 23	Strategies
Mon Oct 28	(Strategies contd.)
Sun Nov 3	(no lecture; turn in homework)		Homework 4 .pdf file
Mon Nov 4-5	Election Day Holiday
Wed Nov 6	The Par Monad
Mon Nov 11	The Haskell Tool Stack Repa: Regular Parallel Arrays
Wed Nov 13	Accelerate: GPU Arrays
Sun Nov 17	(no lecture; turn in homework)		Project Proposal
Mon Nov 18	The Lambda Calculus
Wed Nov 20	(Lambda contd.)
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

tictac: Tic Tac Toe Solver Proposal Report Presentation Files Milin Saini

unmatched: Nuneke Kwetey

2048solver: 2048 Game (FQ) Proposal Report Presentation Files Linh Bui

convex_hull: Convex Hull (FQ) Proposal Report Presentation Files Claudia Cortell, Kyle Edwards, and Avighna Suresh

Convex_Hull: Convex Hull (FQ) Proposal Report Presentation Files Henry Lin and George Morgulis

halma: Board Game (FQ) Proposal Report Presentation Files Luana Liao and Catherine Lyu

Mandelbrot: Mandelbrot Set Generator (FQ) Proposal Report Presentation Files Isabel Tu and Max Zhang

Minimax-Mancala: Mancala Game (FQ) Proposal Report Presentation Files Caiwu Chen, Sindhu Krishnamurthy, and Daniel Manjarrez

Nonogram: Puzzle Solver (FQ) Proposal Report Presentation Files Jittisa Kraprayoon and Dorothy Nelson

Othello: Game Solver (FQ) Proposal Report Presentation Files Noam Hirschorn and Daniel Ivanovich

team_nik: Chess Solver (FQ) Proposal Report Presentation Files Nikolaus Holzer

tsp-sat: DPLL-based SAT solver (FQ) Proposal Report Presentation Files Yixuan LI, Jiaqian Li, and Phoebe Wang

a-star-traveling-salesman: Traveling Salesman (SB) Proposal Report Presentation Files Adele Bai and Vincent Mutolo

KQueens: N-Queens problem (SB) Proposal Report Presentation Files Viktor Basharkevich and Phillip Yan

maze-solver: A* For Solving Mazes (SB) Proposal Report Presentation Files Mohsin Rizvi

molecular-dynamics: Multiple Particle Simulation (SB) Proposal Report Presentation Files Pavan Ravindra

MVC: Minimum Vertex Cover (SB) Proposal Report Presentation Files Tony Giannini, Andre Mao, and Minh Hien Tran

parallel-DFS: Maze Solver (SB) Proposal Report Presentation Files Letong Dai and Samyukkta Suryanarayanan

ShockNet: Financial Simulator (SB) Proposal Report Presentation Files Nick Ching and Erica Choi

document_retrieval: Search by similarity (SE) Proposal Report Presentation Files Mooizz Abdul and Samhit Chowdary Bhogavalli

assignment: Parallel Auction Algorithm (SE) Proposal Report Presentation Files Haolin Guo, Yuanqing Lei, and Ava Penn

sat-solver: SAT Solver (SE) Proposal Report Presentation Files Kevin Durand, Max Hahn, and Jonathan Tavarez

tsp-ga: Traveling Salseman Problem with Genetic Algorithms (SE) Proposal Report Presentation Files Timothy Johns

Word-Search-2: Serpentine Word Search (SE) Proposal Report Presentation Files Keith Lo, Ardrian Wong, and Sean Zhang

rubik: Rubik's Cube Solver (SE) Proposal Report Presentation Files Roberto Brera, Matthew Rosenberg, and Hongcheng Tian

My favorites

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

• Miran Lipovaca. Learn You a Haskell for Great Good! No Starch Press, 2011.
• Bryan O'Sullivan, Don Stewart, and John Goerzen. Real World Haskell. O'Reilly, 2008.
• Simon Marlow. Parallel and Concurrent Programming in Haskell. O'Reilly, 2013.

Links

• PFP from Fall 2023

• PFP from Fall 2022

• PFP from Fall 2021

• PFP from Fall 2020

• PFP from Fall 2019