speed-comparison

This https://github.com/niklas-heer/speed-comparison/blob/master/... and this https://github.com/niklas-heer/speed-comparison/blob/master/... from parent comment

> Nim "cheats" in a similar way C and C++ submissions do: -fno-signed-zeros -fno-trapping-math I don't see these flags in Nim compilation config. The only extra option used is "-march=native"[0]. [0] https://github.com/niklas-heer/speed-comparison/blob/9681e8e...

Per the rules[0]: "Use idiomatic code for the language. Compiler optimizations flags are fine." Agree with the rest of your comment. [0]: https://github.com/niklas-heer/speed-comparison#rules

From https://github.com/niklas-heer/speed-comparison/blob/master/... // [I removed the error checking] def pi_accuracy(value_str): """Calculate accuracy of computed pi value. Returns the number of correct decimal places (higher is better). """ value = float(value_str) # math.pi is available in MicroPython accuracy = 1 - (value / math.pi) return -math.log10(abs(accuracy))

Reading https://github.com/niklas-heer/speed-comparison/blob/master/..., I think the only overflow checks could be in for i in 2...rounds+2 and I would hope/expect the compiler to be smart enough to know that it only has to check “rounds+2” once there. Swift isn’t exactly new anymore, and it’s supported by a large company. What do I overlook?

C# places quite low though (at ~1s). C# (SIMD) that you see towards the top is more complicated: https://github.com/niklas-heer/speed-comparison/blob/master/.... In your metric winner is Nim: https://github.com/niklas-heer/speed-comparison/blob/master/... (followed by Julia and D).

The delta there is because the Rust 1.92 version uses the straightforward iterative code and the 1.94-nightly version explicitly uses std::simd vectorization. Compare the source code: https://github.com/niklas-heer/speed-comparison/blob/master/... https://github.com/niklas-heer/speed-comparison/blob/master/...

C# wins hands down in the performance / lines of code metric. There is very little superfluous or that cannot be inferred by the compiler here: https://github.com/niklas-heer/speed-comparison/blob/master/...

It's an extremely simple algorithm, just one loop with an iterated expression inside it. You can check the source code at: https://github.com/niklas-heer/speed-comparison/tree/master/... It's true this is a microbenchmark and not super informative about "Big Problems" (because nothing is). But it absolutely shows up code generation and interpretation performance in an interesting way. Note in particular the huge delta between rust 1.92 and nightly. I'm gonna guess that's down to the autovectorizer having a hole that the implementation slipped through, and they fixed it.

Constructively, I would suggest some areas for study: - relative speeds of programming languages (https://github.com/niklas-heer/speed-comparison) - database indexing (https://stackoverflow.com/questions/1108/how-does-database-i...) - numbers everyone should know (https://news.ycombinator.com/item?id=39658138) And note that databases are generally written in C.

Another language comparison repo that's been going for longer with more languages https://github.com/niklas-heer/speed-comparison.

Page wants you to register to read the full article Direct link to report: https://www.cncf.io/reports/the-state-of-webassembly-2023/ And the benchmark also mentioned there: https://github.com/niklas-heer/speed-comparison

> has near native performance what do you mean by this? https://github.com/niklas-heer/speed-comparison https://benchmarksgame-team.pages.debian.net/benchmarksgame/...