I heard some Python guys said that Julia’s niche will get narrower over time. The argument is that the basic built-in functions of Python are written in C so they are faster than the Julia counterparts. For large packages, people can use C++ to get extreme performance with its black magic. Julia is …

No. While Steven pointed out that because Julia uses the same LLVM compiler that compiles C code, and therefore isn’t generically faster than C, the converse is also true: C isn’t generically faster than Julia. In almost all situations I’ve encountered or cared enough to invest in, Julia = C for per…

[image] GunnarFarneback: In my opinion yes, but not by all that much. Well optimized C code that uses all the tricks in the book is still difficult to match. For fun I’ve tried porting libdeflate to Julia as literally as possible and I still haven’t done better than 2-3 times slower. Probably th…

[image] ChrisRackauckas: Have you slapped an AVX macro on inner parts? Inner parts of libdeflate? I have only ported the decompression and the hottest loop of that is decoding a variable length code from bits. That is extremely sequential, you don’t even know where the next codeword begins un…

On my machine (macOS) I get, for log: 5.065 ns (0 allocations: 0 bytes) 4.438 ns (0 allocations: 0 bytes) 4.188 ns (0 allocations: 0 bytes) and for exp: 7.292 ns (0 allocations: 0 bytes) 4.805 ns (0 allocations: 0 bytes) 4.421 ns (0 allocations: 0 bytes) Any idea why the Julia version appears to…

[image] Yifan_Liu: I heard some Python guys said that Julia’s niche will get narrower over time. … Over time, this niche will get narrower because Python’s built-in functions and large packages both become more comprehensive. Is this true? History has shown that julia (and all other languages)…

There’s already a lot in this thread, but just to address the points directly: Is this true? No, neither the conclusion nor any of the assertions are true. built-in functions of Python are written in C so they are faster than the Julia counterparts I wonder exactly what the assertion here wa…

A small test script comparing element-wise operations in Numpy and Julia: https://gist.github.com/lungben/1bdebc2a7452df6d4a1d0b6a30160633 For array sizes up to 1,000 elements, Julia is always faster on my machine. For 100,000 elements, the picture is mixed: …

So if you are testing the lower-level linear operations, then the particular library (BLAS versus MKL) and its version may play a role.

I tested only element-wise operations, which are to my knowledge not using BLAS/MKL. For matrix multiplication, etc. you are correct - there Numpy is probably faster on Intel machines because it uses MKL as default (for Conda).

[image] carstenbauer: Any idea why the Julia version appears to be slightly slower for me? I could understand why the system library functions are different/faster on macOS but LLVM? Hmm, I think LLVM is just calling the system libraries, but with a faster calling convention, using just a jmp …

On my MacOS (intel) machine everything in Julia 1.6-rc1 is faster than numpy 1.19.5 in Python 3.8.5.

Julia's applicable context is getting narrower over time?

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Julia's applicable context is getting narrower over time?

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