Task: Sum Array

In labs/lab-06/tasks/sum-array/support/c/sum_array_threads.c we spawned threads “manually” by using the pthread_create() function. This is not a syscall, but a wrapper over the common syscall used by both fork() (which is also not a syscall) and pthread_create().

Still, pthread_create() is not yet a syscall. In order to see what syscall pthread_create() uses, check out this section.

Most programming languages provide a more advanced API for handling parallel computation.

Array Sum in Python

Let’s first probe this by implementing two parallel versions of the code in sum-array/support/python/sum_array_sequential.py. One version should use threads and the other should use processes. Run each of them using 1, 2, 4, and 8 threads / processes respectively and compare the running times. Notice that the running times of the multithreaded implementation do not decrease. This is because the GIL makes it so that those threads that you create essentially run sequentially.

The GIL also makes it so that individual Python instructions are atomic. Run the code in labs/lab-06/tasks/race-condition/support/python/race_condition.py. Every time, var will be 0 because the GIL doesn’t allow the two threads to run in parallel and reach the critical section at the same time. This means that the instructions var += 1 and var -= 1 become atomic.

If you’re having difficulties solving this exercise, go through this reading material.