I ran some of my performance benchmarks (overlay, PIP) am seeing roughly equal or slightly better performance on the DD branch. So, sounds like a win to me.
Dan On Wed, Apr 15, 2020 at 5:17 PM Paul Ramsey <pram...@cleverelephant.ca> wrote: > So, we ran into some issues with GEOS 3.8 on a Power architecture, and > based on what tests failed, I suspect the issue is with our use of ttmath. > > In particular, this comment says to me (and just eyeballing what's going > on) that there is some in-baked assumptions about endianness. > > > https://github.com/libgeos/geos/blob/master/include/geos/algorithm/ttmath/ttmathbig.h#L2607-L2609 > > The only reason we're using ttmath is because Mateusz happened to choose > it for his initial experiments on supporting the higher precision code in > JTS. Turns out there's no reason we cannot use exactly the same routines as > JTS does, though, and they have NO ENDIAN assumptions in them, so they'll > be portable. > > The port of the JTS code is here: > > https://github.com/libgeos/geos/pull/303 > > Amazingly, all the regression tests still pass. > > Also, just based on running the full test suite, the DD code seems 5-10% > faster than ttmath. This isn't entirely surprising, since ttmath is an > arbitrary precision system, while doubledouble uses nothing but standard > double math and the assumption that all double representations and > operations are to IEEE spec. > > I'm going to port the JTS unit tests so we have a little more foundation > under this work, but then I'd like to rip out ttmath in 3.9. > > I'm a little worried that we cannot support big-endian machines in 3.8, so > I think we might consider back-porting this work to 3.8 and switch over to > it for big-endian architectures. > > Paul > > _______________________________________________ > geos-devel mailing list > geos-devel@lists.osgeo.org > https://lists.osgeo.org/mailman/listinfo/geos-devel
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