53 lines
1.9 KiB
Markdown
53 lines
1.9 KiB
Markdown
# Exercise 2a: multithreading with NumPy
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Objective: investigate speed-up of numpy code with multiple threads.
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```HINT``` Use `htop` in your terminal to track what the CPUs are doing.
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## First
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The script `heavy_computation.py` performs some matrix calculations with numpy.
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You can change the number of threads that numpy uses for the calculation
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using the `OMP_NUM_THREADS` environment variable like this:
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```
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OMP_NUM_THREADS=7 python heavy_computation.py
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```
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The script will also measure the time to run the calculation and will save
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the timing results into the `timings/` folder as a `.txt` file.
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**TASK**: Execute the script `heavy_computation.py`, varying the numbers of threads.
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You will plot the resulting calculating times in the second part below.
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**QUESTION**
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> What happens if `OMP_NUM_THREADS` is not set? How many threads are there? Why?
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## Second
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In `plot.py`, we have given code that will load all of the timing data in `timings/`.
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**TASK**: Add code to plot of the execution duration vs. the number of threads
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Open a PR with your plotting code and post your plots in the conversation, don't upload binaries to the Git remote!
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**OPTIONAL TASK**: Add code to calculate and plot the speed-up time compared
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to single-threaded execution. Include your code and plot in the PR.
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**QUESTIONS**
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> What does the result tell us about the optimum number of threads? Why?
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> Does it take the same time as your colleagues to run? Why?
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## Optional tasks
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Investigate the runtime variability. Systematically run multiple instances with the same number of threads by modifying `heavy_computation.py`.
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How is the runtime affected when the problem becomes bigger? Is the optimum number of threads always the same?
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How is the runtime affected when the memory is almost full? You can fill it up by creating a separate (unused) large numpy array.
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How about running on battery vs. having your laptop plugged in?
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