June 11, 2020 Re: Three articles on D | ||||
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Posted in reply to tastyminerals | On Tuesday, 9 June 2020 at 21:30:24 UTC, tastyminerals wrote: > FYI, I have a couple of Julia benchmarks timed against NumPy here: > https://github.com/tastyminerals/mir_benchmarks#general-purpose-multi-thread Interesting. There is a recent Julia package called LoopVectorization which by all accounts performs much better than base Julia: https://discourse.julialang.org/t/ann-loopvectorization/32843 |
June 11, 2020 Re: Three articles on D | ||||
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Posted in reply to data pulverizer | On Thursday, 11 June 2020 at 22:11:41 UTC, data pulverizer wrote:
> On Tuesday, 9 June 2020 at 21:30:24 UTC, tastyminerals wrote:
>> FYI, I have a couple of Julia benchmarks timed against NumPy here:
>> https://github.com/tastyminerals/mir_benchmarks#general-purpose-multi-thread
>
> Interesting. There is a recent Julia package called LoopVectorization which by all accounts performs much better than base Julia: https://discourse.julialang.org/t/ann-loopvectorization/32843
True, a very solid improvement indeed.
Sigh, wish D received as much attention as Julia continues to get.
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June 12, 2020 Re: Three articles on D | ||||
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Posted in reply to tastyminerals | On Thursday, 11 June 2020 at 23:08:45 UTC, tastyminerals wrote: > On Thursday, 11 June 2020 at 22:11:41 UTC, data pulverizer wrote: >> On Tuesday, 9 June 2020 at 21:30:24 UTC, tastyminerals wrote: >>> FYI, I have a couple of Julia benchmarks timed against NumPy here: >>> https://github.com/tastyminerals/mir_benchmarks#general-purpose-multi-thread >> >> Interesting. There is a recent Julia package called LoopVectorization which by all accounts performs much better than base Julia: https://discourse.julialang.org/t/ann-loopvectorization/32843 > > True, a very solid improvement indeed. > Sigh, wish D received as much attention as Julia continues to get. It sounds like @avx for Julia is a bit like @fastmath [1]. I was re-reading this [2] recently. You may find interesting. [1] https://wiki.dlang.org/LDC-specific_language_changes#.40.28ldc.attributes.fastmath.29 [2] http://johanengelen.github.io/ldc/2016/10/11/Math-performance-LDC.html |
June 13, 2020 Re: Three articles on D | ||||
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Posted in reply to jmh530 | On Friday, 12 June 2020 at 00:24:39 UTC, jmh530 wrote: > It sounds like @avx for Julia is a bit like @fastmath [1]. I was re-reading this [2] recently. You may find interesting. > > [1] https://wiki.dlang.org/LDC-specific_language_changes#.40.28ldc.attributes.fastmath.29 > [2] http://johanengelen.github.io/ldc/2016/10/11/Math-performance-LDC.html Interesting. I didn't know that fast math vectorized calculations - automatically using SIMD. That feature isn't mentioned on the LLVM fast math documentation https://llvm.org/docs/LangRef.html#fast-math-flags. Julia's approach to SIMD and fast math seems effective - the practice of being able to label individual statements to direct the compiler to optimize those specific statements. |
June 13, 2020 Re: Three articles on D | ||||
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Posted in reply to data pulverizer | On Saturday, 13 June 2020 at 05:29:34 UTC, data pulverizer wrote: > Interesting. I didn't know that fast math vectorized calculations - automatically using SIMD. That feature isn't mentioned on the LLVM fast math documentation https://llvm.org/docs/LangRef.html#fast-math-flags. Julia's approach to SIMD and fast math seems effective - the practice of being able to label individual statements to direct the compiler to optimize those specific statements. p.s. @simd in Julia was written by Intel's Arch Robinson the architect of Intel's Threading Building Blocks. That kind of support is very helpful indeed https://software.intel.com/content/www/us/en/develop/articles/vectorization-in-julia.html. |
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