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# Exercise A: multithreading with NumPy
Objective: investigate speed-up of numpy code with multiple threads.
```HINT``` Use `htop` in your terminal to track what the CPUs are doing.
## First
The script `heavy_computation.py` performs some matrix calculations with numpy.
You can change the number of threads that numpy uses for the calculation
using the `OMP_NUM_THREADS` environment variable like this:
```
OMP_NUM_THREADS=7 python heavy_computation.py
```
The script will also measure the time to run the calculation and will save
the timing results into the `timings/` folder as a `.txt` file.
**TASK**: Execute the script `heavy_computation.py`, varying the numbers of threads.
You will plot the resulting calculating times in the second part below.
**QUESTION**
> What happens if `OMP_NUM_THREADS` is not set? How many threads are there? Why?
## Second
In `plot.py`, we have given code that will load all of the timing data in `timings/`.
**TASK**: Add code to plot of the execution duration vs. the number of threads
**TASK**: Open a Pull Request with your plotting code and post your plots in the
conversation. Don't upload binaries to the Git remote!
**OPTIONAL TASK**: Add code to calculate and plot the speed-up time compared
to single-threaded execution. Include your code and plot in the PR.
**QUESTIONS**
> What does the result tell us about the optimum number of threads? Why?
> Does it take the same time as your colleagues to run? Why?
## Optional tasks
Investigate the runtime variability. Systematically run multiple instances with the same number of threads by modifying `heavy_computation.py`.
How is the runtime affected when the problem becomes bigger? Is the optimum number of threads always the same?
How is the runtime affected when the memory is almost full? You can fill it up by creating a separate (unused) large numpy array.
How about running on battery vs. having your laptop plugged in?