Hi. I needed to implement a topological sort, so I found some implementations on the Internet here: Topological sort - Rosetta Code I initially needed it in Python and experimented with Julia just out of curiosity. Here is a Python code: try: from functools import reduce except: pass da…

It would be pretty easy to replace the strings with integer id’s in this case, since it’s really just using them as keys rather than processing

[image] Liso: It is also my observation/hypothesis that for this kind of work current String implementation in Julia is not so good (if we want speed) in comparison to python’s. Is this just a guess or do you actually have anything to support the hypothesis? Did you do any profiling? What is …

[image] Liso: not discourage people from using generator because “we have it” Using Channels (I don’t know why we are talking about generators in this thread) as a way to implement the iteration protocol could indeed be warned against, perhaps in the language difference section.

[image] kristoffer.carlsson: I don’t know why we are talking about generators in this thread @Liso linked another thread where a performance drop when continuously creating generators was observed. This is akin to the continuous creation of new Sets in the OP - why he referenced that exactly,…

Alright, the example in that post is better on master (4x faster): julia> @btime inversions1($p) 179.826 ns (21 allocations: 640 bytes) 45 compared to 1.0.1: julia> @btime inversions1($p) 717.085 ns (111 allocations: 3.44 KiB) 45 So while there is still a slow-down compared to the hand writt…

Without channels, using just generators is still 20% slower than in python. I think it is problem too. Don’t you agree?

Again, this just seems like it is a mistaken belief that any Python code directly translated to Julia will be (magically) faster even if most of the time in the Python code is running optimized C code. That this is not the case is not a problem in itself. It is not the goal of Julia to be faster tha…

[image] Liso: what does (0.00% GC) mean? Wasn’t just important part of code (GC) not measured here? It was measured and no GC was performed so it took 0% of the time. GC is triggered by allocation so code that doesn’t allocate will never trigger GC. It can’t take time if it doesn’t happen.

But there is: memory estimate: 31.05 KiB allocs estimate: 447 so allocation happened and if we want measure performance impact to system we need to measure GC work too. Is this mean time: 24.255 μs (26.77% GC) GC work caused only by toposort3 function? Or there could be for examp…

FYI Python’s timeit module disable GC during the timing by default: timeit — Measure execution time of small code snippets — Python 3.10.6 documentation Not sure what is the setting in IPython though. It may be fair to enable the GC in Python.

Topological sort (performance)

General Usage Performance

kristoffer.carlsson November 8, 2018, 3:29pm 39

That was from the timing in Performance problem of count? - #13 by Sukera which was linked from Topological sort (performance) - #18 by Liso.

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Topological sort (performance)

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