Re: [Inkscape-devel] performance problems and possible remedies?

19 Sep 2016

      My spreadsheet (benchmark.xlsx) didn't get accepted. Here it is as base64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On Mon, Sep 19, 2016 at 5:25 AM, Yale Zhang <yzhang1985@...400...> wrote:
...
I'm proud to announce that I have completed vectorizing both the IIR
and FIR filters. Speed ups are about 5x for IIR and ~20x for FIR (see
spreadsheet). The code is a monstrosity, but given all the different
cases ( {FIR, IIR} x {int16, float, double} x {RGBA, grayscale} as
Jasper pointed out, it's expected. Earlier, I had only worked on the
IIR, RGBA case, so it wasn't complete. It's unfortunate I let it
languish for 3 years, but now hopefully, everyone can get a smoother
experience.
Please test it out and send some feedback and a path for checking in.
A unit test & benchmark function is included. The accuracy is always
+- 1 intensity level, which should be quite safe.
To build it, please add this to your cmake command:
-DCMAKE_CXX_FLAGS="-DWIN32 -mavx2 -mfma -fpermissive -flax-vector-conversions"
You'll also need to turn on -std=c++14 if compiling the unit test & benchmark
Todo list:
*support processors with SSE2 only? current version only works on an
AVX2 processor
*dynamic dispatch for different CPU types - I guess there should be 3
versions of the functions (SSE2, AVX1, AVX2)
*better ways to deal with SIMD remainders? The current code never
writes past the end of a row, but liberally uses vector loads that go
past the end. This will render memory error checking tools like
AddressSanitizer useless with false positives. Ideally, I'd like all
the Cairo images to be padded to either 16 (SSE2) or 32 bytes (AVX),
but that might not be practical.
*document all the arcane optimization methods (cache blocking,
do-while loops, Duff's device) for posterity. Preferably with SVG
animations
-Yale
On Wed, Mar 6, 2013 at 11:23 AM, Johan Engelen
<jbc.engelen@...2592...> wrote:
...
On 4-3-2013 6:44, Yale Zhang wrote:
...
...  but I'll still go ahead with the
vectorized implementation.
Yes please do! I'm very interested in how to include SIMD code and how
to go about vectorizing code; great if we have an example in our codebase.
Cheers,
   Johan

Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester
Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the
endpoint security space. For insight on selecting the right partner to
tackle endpoint security challenges, access the full report.
http://p.sf.net/sfu/symantec-dev2dev
_______________________________________________
Inkscape-devel mailing list
Inkscape-devel@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/inkscape-devel