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all programs are single threaded unless otherwise specified.
Does Python have the ability to specify loops that should be executed in parallel, as e.g. Matlab uses
parforinstead offor?python has way too many ways to do that.
asyncio,future,thread,multiprocessing…Of the ways you listed the only one that will actually take advantage of a multi core CPU is
multiprocessingyup, that’s true. most meaningful tasks are io-bound so “parallel” basically qualifies as “whatever allows multiple threads of execution to keep going”. if you’re doing numbercrunching in pythen without a proper library like pandas, that can parallelize your calculations, you’re doing it wrong.
I’ve used multiprocessing to squeeze more performance out of numpy and scipy. But yeah, resorting to multiprocessing is a sign that you should be dropping into something like Rust or a C variant.
Most numpy array functions already utilize multiple cores, because they’re optimized and written in C
It’s safe to assume that any non-trivial program written in Go is multithreaded
And yet: You’ll still be limited to two simultaneous calls to your REST API because the default HTTP client was built in the dumbest way possible.
The client object or the library?
… Is this a trick question? The object, provided by the library (net/http which is about as default as they come) sets “DefaultMaxIdleConnsPerHost” to 2. This is significant because if you finish a connection and you’ve got more than 2 idles, it slams that connection close. If you have a lot of simultaneous fast lived requests to the same IP (say a load balanced IP), your go programs will exhaust the ephemeral port list quickly. It’s one of the most common “gotchas” I see where Go programs work great in dev and blow themselves apart in prod.
https://dev.to/gkampitakis/http-connection-churn-in-go-34pl is a fairly decent write up.
Really? Huh, TIL. I guess I’ve just never run into a situation where that was the bottleneck.
