Obvious advice perhaps, but so easily forgotten somehow...
OK, this could be big for Python. Let's see how they execute this plan. It carries some risks as well, but they seem well aware of them.
Interesting optimization on this somewhat common data structure.
Unsurprisingly, it's not as simple as it sounds. Type hints in Python can be used for various reasons but performances is rarely the main motives. It'd need other adjustments to the runtime. People are working on it, and this article is an interesting dive on how things work under the hood.
This compilation technique brings very interesting results. Hopefully should find its way in some JIT compilers.
Interesting research turning to genetic algorithms to optimize bytecode handler dispatchers.
Deep dive on a proper benchmarking and implementation for 1M task on the Erlang runtime. Clearly the previous benchmark had room for improvements.
Nice and thorough workshop on vectorization, where it comes from, what it can do and how you can write code which is easier to vectorize for the compiler.
Doesn't give the whole picture (memory isn't the only important parameter) but interesting results nonetheless. A few surprises in there, Java and C# do much better than one might assume for instance.
Interesting take. Will it lead to paying more attention to performance in software? Will it be the rise of the specialized CPUs? Time will tell.
Good explanation of how flame graphs are produced and how to read them. Gives a few tips on what to look for to optimize.
Interesting exploration on the performance of SSDs regarding write patterns. Turns out sequential IO is still a thing, just for a different reason than with good old HDDs.
Very interesting to see that move to owned hardware... turns out that not only the invoice is smaller in their case but the performances are much better as well.
Nice set of tips, I knew a few but not all of them. The discussion around CTEs is interesting.
Nice exploration of false sharing on performances in several hardware scenarii. A couple of surprises along the way.
Shouldn't come as a surprise if you paid attention to C++ evolutions for the past 30 years. We're now reaping the fruits though, so it's really become easy to keep both options in sight when designing. This is especially important for performance sensitive code.
Nothing really new here (apart from the "how easy it is these days!")... Still it needs to be reminded on a regular basis. :-)
Nice post explaining the common algorithms used for load balancing. Each having their own trade offs of course. Well done with tiny simulations.
Polars really looks like a nice alternative to Pandas with a nice upgrade path from data exploration to production.
Nice walk through for a use of PyO3 to make some Python code much faster. Nice to see how useful py-spy turn out to be in such scenarii as well.
Very interesting conversation between Uncle Bob and one of the recent critics of his work regarding performance. I like how he admits some faults in the way he presents things and try to improve for later rather than trying to be right. Some people should learn from that. There's clearly a tension between performance and what is described in Clean Code, it'd be pointless to deny it.