TDM 30200: Project 3 — 2023

joblib is a Python library that makes many parallelization tasks easier. Run the following code in three separate code cells. But, before you do, look at the code and write down approximately how much time you think each cell will take to run. 1 call to run_for will take roughly 2.8 - 3.2 seconds on an Anvil cpu. Take note that we currently have 1 cpu for this notebook.

Were you correct? Great! We only have 1 cpu, so regardless if we chose to use 2 threads or 2 processes, only 1 cpu would be used and 1 thing executed at a time.

threading: This backend for joblib will use threads to run the tasks. Even though we only have a single cpu, we can still create as many threads as we want, however, due to Python’s GIL (Global Interpreter Lock), only 1 thread can execute at a time.

multiprocessing: This backend for joblib will use processes to run the tasks. In the same way we can create as many threads as we want, we can also create as many processes as we want. A process is created by an os function called fork(). A process can have 1 or more threads or threads of execution, in fact, typically a process must have at least 1 thread. Threads are much faster and take fewer resources to create. Instead of fork() a thread is created by clone(). A single cpu can have multiple processes or threads, but can only execute 1 task at a time. As a result, we end up with the same amount of time used to run.

When writing a program, you could make your program create a process that spawns multiple threads. Those threads could then each run in parallel, 1 per cpu. Alternatively, you could write a program that has a single thread of execution, and choose to execute the program n times creating n processes that each run in parallel, 1 per cpu. The advantage of the former is that threads are lighter weight and take less resources to create, an advantage of the latter is that you could more easily distribute such a program onto many systems to run without having to worry about how many threads to spawn based on how many cpus you have available.

Okay, let’s take a look at this next example. Run the following (still with just 1 cpu).

Did you get it right this time? If not, it is most likely that you thought all 3 would take about 8 seconds. We only have 1 cpu, after all. Let’s try to explain.

threading: Like we mentioned before, due to Python’s GIL, we can only execute 1 thread at a time. So why did our example only take about 2 seconds if only 1 thread can execute at a time? time.sleep is a function that will release Python’s GIL (Global Interpreter Lock) because it is not actually utilizing the CPU while sleeping. It is not the same as running an intensive loop for 2 seconds (like our previous example). Therefore the first thread can execute, the GIL is released, the second thread begins execution, rinse and repeat. The only execution that occurs is each thread consecutively starting time.sleep. Then, after about 2 seconds all 4 time.sleep calls are done, even though the cpu was not utilized much at all.

multiprocessing: In this case, we are bypassing the restrictions that the GIL imposes on threads, BUT, time.sleep still doesn’t need the cpu cycles to run, so the end result is the same as the threading backend, and all calls "run" at the same time.

Okay, let’s try something! Save your notebook (and output from question 1), and completely close and delete your ondemand session. Then, launch a new notebook, but instead of choosing 1 core, choose 4. Run the following code, but before you do, guess how much time each will take to run.

How did you do this time? You may or may not have guessed, but the threading version took the same amount of time, but the multiprocessing backend was just about 4 times faster! What gives?

Whereas Python’s GIL will prevent more than a single thread from executing at a time, when joblib uses processes, it is not bound by the same rules. A process is something created by the operating system that has its own address space, id, variables, heap, file descriptors, etc. As such, when joblib uses the multiprocessing backend, it creates new Python processes to work on the tasks, bypassing the GIL because it is n separate processes and Python instances, not a single Python instance with n threads of execution.

In general, Python is not a good choice for writing a program that is best written using threads.

Okay, great, let me parallelize something! Okay, sounds good.

The task is to count all of the lines in all of the files in /anvil/projects/tdm/data/flights/subset/*.csv, from the 1987.csv to 2008.csv, excluding all other csvs.

First, write a non-parallelized solution that opens each file, counts the lines, adds the count to a total, closes the file, and repeats for all files. At the end, print the total number of lines. Put the code into a code cell and time the code cell using %%time magic.

Now, write a parallelized solution that does the same thing. Put the code into a code cell and time the code cell using %%time magic.

Make sure you are using a Jupyter Lab session with 4 cores.

Parallelize the task and function that you have been writing about in the past 2 projects. If you are struggling or need help, be sure to ask for help in Piazza! If after further thinking, what you specified in the previous project is not easily parallelizable, feel free to change the task to some other, actually parallelizable task!

Please time the task using %%time magic, both before and after parallelizing the task — after all, its not any fun if you can’t see the difference!