Can I tangentially point out without much connection to this benchmark more than any of the others recently, that one of the great things about PyPy is that if you have a thing and you run it on PyPy, you can usually pop in the IRC channel and often get even <i>more</i> tips on how to tune it to be even faster?<p>There are the simple tips like "write everything in Python where possible, don't use C extensions" like the OP noticed, but even after you've made the decision on using PyPy there are often specific performance characteristics of the PyPy implementation that can be really helpful to keep in mind, and it's a great resource to try and take advantage of (human interaction with PyPy developers like fijal who care about making things fast).
>Moreover PyPy doesn’t kill my CPU as CPython does so in a meantime I could normally use my laptop<p>You're not supposed to "use your laptop" during a benchmark.
Great to see real world use cases, and very encouraging to see PyPy performing so well. I'll definitely be trying it on my future compute-intensive projects.
I'm reminded of when I first started following the PyPy project, and I thought it would be impressive if they could ever get close to CPython's speed. They've done something truly amazing.<p>I hope the python community someday coalesces around a single version. I generally can't take advantage of all their awesome work because the libraries I depend on (e.g. pandas) won't run in pypy.