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Making Julia as Fast as C++ (2019)

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Re: Making Julia as Fast as C++ (2019)

#61

Post by sheepscreek »

tialaramex wrote: post #38
HarHarVeryFunny wrote: post #15

Earlier quoted context omitted.

I'd have to guess that this is because of ease of use. C++ lets you get as close to the metal as you choose to, so there is no reason why a C++ solution shouldn't be at least as fast as one written in any other language, and yet ... Of course it also depends on what additional libaries you are using, especially when it comes to parallel/GPU programming in C++, but easy to believe that Julia out of the box makes it ea…

> C++ lets you get as close to the metal as you choose to This only ends up being true (for any language, but it's too often cited for C++) in a pretty useless Turing Tarpit sort of sense. So it's not "no reason" it's just sometimes impractical to solve some problems as well in C++ as in a language that was better suited. Now people do do impractical things sometimes. It's not very practical to swim across the Englis…

Drawing from the analogies, what’s the Julia equivalent of them?

Re: Making Julia as Fast as C++ (2019)

#63

Post by ilayn »

adrian_b wrote: post #54
neutrinobro wrote: post #13

Earlier quoted context omitted.

Hardly seems worth the effort, perhaps things have improved since 2019. It would be interesting to see an updated benchmark, but if your going to end up with code that looks like C++ to get proper performance, you might as well write it in C++. My biggest problem with Julia is that they decided to use column-major indexing for multi-dimensional arrays (i.e. FORTRAN/MATLAB style). This makes interoperability with C/C+…

Actually the column-major order of Fortran is more efficient for some linear algebra operations than the order of C, which has been inherited by many modern languages that do not care about high performance in scientific computations. So I would say that the culprit for interoperability is C and its descendants, not Fortran or Julia. The designers of C and of the languages that have imitated C have not given any thou…

> Actually the column-major order of Fortran is more efficient for some linear algebra operations than the order of C, which has been inherited by many modern languages that do not care about high performance in scientific computations.

This is a plausible assumption to make but unfortunately it is not true at large. Especially when the traditional sizes are exceeded say n >= 2000 certain operations such as LU can be improved in terms of performance with C-major arrays. However the correct statement is you lose at some place you win at other. There are certainly linalg operations that F-major can give you more performance. However it is also true for C-major layout.

In your example matrix vector product or any BLAS2 or BLAS3 level operations you can also swap out the for loop order to convert things around (row*col buffer multiplication vs sum of weighted column sum interpretation). In particular matrix norm operations are the only exceptions (abs column sum, row abs sum etc.) that certain norms prefer certain orders. In fact if you go into the Goto method deep enough you'll see that internal order is a bit like Morton ordering to fit things into L1 Cache.

The reason why column-major is preferred is historical and requires more surgery to get it running with C-major ordering. Trust me I tried but it's too much work to gain not so much. Maybe someday when I retire I can attempt it. Hence I kept it column major in my retranslation of LAPACK https://github.com/ilayn/semicolon-lapack

Instead I implemented a "high"-performance AVX2 matrix transpose operation so that swapping the memory layout is trivial compared to the linalg cost.

Re: Making Julia as Fast as C++ (2019)

#64

Post by Woodi »

Over the years there already was almost the identical articles about making in language X program as fast as C or C++... And results was exactly the same: write C/C++ style programs!

Why ?

Because of CPU's architecture - given CPU one just need to structure code in a way CPU can perform efficiently! Is it such surprising that all sugar and multi-functional smartness have cost of all that if's and loops like maps? CPU is just rock stupid and can't do anything else!

That's from where all that specialized instructions are coming and programs just need to be structured or compiled to CPU arch way to perform as fast as CPU and rest of hardware allows...

And there are some "Java machines" and that is exaclty the same story: use CPU native lang :) As much as posible.

So: give us better cpus pls :)

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