Submit material to Homework 14
The deadline is extended to 9am Mon. I will grade whatever has been submitted at that time.
"The goal of the contest is to write code that is as readable, clear, innocent and straightforward as possible, and yet it must fail to perform at its apparent function. To be more specific, it should do something subtly evil. Every year, we will propose a challenge to coders to solve a simple data processing problem, but with covert malicious behavior. Examples include miscounting votes, shaving money from financial transactions, or leaking information to an eavesdropper. The main goal, however, is to write source code that easily passes visual inspection by other programmers."
Moral: After early disasters, sometimes you can eventually get things to work.
Example of a well written paper: https://wrfranklin.org/p/239-marcelo-gpu-predicates-2021.pdf
How not to write: The Bulwer Lytton Fiction Contest has challenged participants to write an atrocious opening sentence to the worst novel never written.
Paper format: one good idea is to use the IEEE conference format . It allows either latex or MS word. Submit the PDF paper to gradescope.
If your final project is building on, or sharing with, another course or project (say on GitHub), then you must give the details, and say what's new for this course.
If you're implementing something:
Lorena Barba IPDPS21 keynote May 20, 2021 (37:11)
Keynote at the IEEE International Parallel and Distributed Processing Symposium, May 19, 2021
Abstract Thirty years ago, David Bailey published a humorous piece in the Supercomputing Review magazine, listing 12 ways of presenting results to artificially boost performance claims. That was at a time when the debate was between Cray "two-oxen" machines versus parallel "thousand-chickens" systems, when parallel standards (like MPI) were still unavailable, and the Top500 list didn't yet exist. In the years since, David and others updated the list of tricks a few times, notably in 2010–11 (when the marketing departments of Intel and Nvidia were really going at each other) Georg Hager in his blog and Scott Pakin in HPC Wire. Heterogeneity of computing systems has only escalated in the last decade, and many remiss reporting tactics continue unabated. Alas, two new ingredients have entered into the mix: wide adoption of machine learning techniques both in the science applications and systems research; and a swell of concern over reproducibility and replicability. My talk will be a new twist on the 12 ways to fool the masses, focusing on how researchers in computational science and high-performance computing miss the mark when conducting or reporting their results with poor reproducibility. By showcasing in a lighthearted manner a set of anti-patterns, I aim to encourage us to see the value and commit to adapting our practice to achieve more trustworthy scientific evidence with high-performance computing.
There's a link to the slides there.
I'm open to questions and discussions about any legal ethical topic. Even after you graduate.