I'm a beginner with Python and I'm currently researching gravitational waves. I'm trying to use Jupyter Notebook on my Windows machine with Ubuntu, but I'm facing a frustrating issue: the Markov Chain Monte Carlo (MCMC) task takes about 30 minutes to run, while my friend's MacBook Pro with an M1 chip completes the same task in just 3 minutes. I'm wondering if Ubuntu or Jupyter is not fully utilizing my system's capabilities. I'm using an ASUS ROG STRIX with an AMD Ryzen 9 processor and Radeon graphics, which seems powerful enough. Any tips or insights on why this might be happening?
3 Answers
You might want to run `np.show_config()` on both machines to check the configurations of your NumPy setup. Sometimes the issue could be related to libraries, not hardware. If you notice that your setup isn't using optimized libraries like OpenBLAS or MKL, that could really slow things down.
It’s tough to say for sure without seeing your code. One thing to keep in mind is that if you’re running your setup in WSL, the GPU might not be fully accessible, which can be a big factor in computational tasks like MCMC. Just double-checking your Python environment can help a lot!
From a quick search, it seems the M1 chip can run some tasks slightly better, but it really depends on your entire setup. This includes the Python version and what else might be running in the background. You might also want to check if your configuration settings allow you to utilize multiple threads effectively.
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