45 lines
1.5 KiB
Markdown
45 lines
1.5 KiB
Markdown
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# Exercise 2b
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Execute numpy code with multiple threads.
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```NOTE``` Remember to use `htop` in your terminal to track what the CPUs are doing.
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## First
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Use the script `heavy_computation.py` with different numbers of threads.
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`OMP_NUM_THREADS` can be used to override the number of threads used:
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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 save the timing results into a `timings/` folder as `.txt` files.
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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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Plot the timing results from the first part, we wrote the IO for you in `timing_plot.py`.
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1. Plot a graph of execution duration vs. the number of threads
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2. Plot the execution speedup with respect to running a single-threaded process
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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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> 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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## Extra
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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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### Extra extra
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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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