ghc2024-vectorization-workshop

Set-Up

Option 1: From your local Python3 shell:

0. Clone this repo (optionally: fork it to your github first)

git clone <>

1. Enter the directory and make your virtual environment (any Python3 version should be fine. Here I use 3.9)

cd ghc2024-vectorization-workshop
python3.9 -m venv venv

2. Activate your new virtual environment

source venv/bin/activate

3. Install required packages from the requirements.txt

python -m pip install -r requirements.txt

Option 2: In a Google Colab Notebook

1. Open a new Google Colab Notebook (https://colab.research.google.com). You will need to be signed into your own Google account

2. copy-paste the code from utils.py into the first cell and run

Writing and testing the functions

Option 1: From your local Python3 shell:

1. Open the q#.py in your text editor, according to the question (q1 through q7). For the given question, put your optimized code in the vec_* function where it says pass # insert your code here.

2. To test that your function works and compare the speed (for relevent questions) by running the test_run.py script:

python test_run.py

Note: if you're having trouble with one function and want to skip to the next one, just use # to comment out that line in the test_run.py

If your function matches the desired output, then you should see either a "Success" message, or a printout of the timing differences.

Option 2: In a Google Colab Notebook

1. Copy the code from the q#.py for your corresponding question (q1 through q7). Be sure to include the imports too, EXCEPT **do not include the line from util import print_time_results, time_funcs. You have already pasted these functions into your notebook during the Setup.

2. Update the vec_* function where it says pass # insert your code here.

3. Run the test_* function to determine if your output is satisfactory, and what the speedup is (for relevent questions). You won't use the test_run.py script if you're working in a Notebook; just copy-paste the test_ functions directly and run them.

Brag about your gains!

1. Go to the Google Form https://forms.gle/9xrjTUSEDozPm4kJ6 and report the speed-up factor you acheived for each function! We'll shout-out the biggest speed-ups live in the workshop.