Hi Benoit, Benoit Jacob wrote: > No, because _we_ are serious about compilation times, unlike other c++ > template libraries. But granted, compilation times are not as short as > a plain C library either. >
I concede it is not as bad as the heavily templated libraries in boost. But C++ is just horribly slow to compile, at least with g++ - in scipy, half of the compilation time is spent for a couple of C++ files which uses simple templates. And the compiler takes a lot of memory during compilation (~ 300 Mb per file - that's a problem because I rely a lot on VM to build numpy/scipy binaries). > Eigen doesn't _require_ any SIMD instruction set although it can use > SSE / AltiVec if enabled. > If SSE is not enabled, my (very limited) tests show that eigen does not perform as well as a stock debian ATLAS on the benchmarks given by eigen. For example: g++ benchBlasGemm.cpp -I .. -lblas -O2 -DNDEBUG && ./a.out 300 cblas: 0.034222 (0.788 GFlops/s) eigen : 0.0863581 (0.312 GFlops/s) eigen : 0.121259 (0.222 GFlops/s) g++ benchBlasGemm.cpp -I .. -lblas -O2 -DNDEBUG -msse2 && ./a.out 300 cblas: 0.035438 (0.761 GFlops/s) eigen : 0.0182271 (1.481 GFlops/s) eigen : 0.0860961 (0.313 GFlops/s) (on a PIV, which may not be very representative of current architectures) > It is true that with Eigen this is set up at build time, but this is > only because it is squarely _not_ Eigen's job to do runtime platform > checks. Eigen isn't a binary library. If you want a runtime platform > switch, just compile your critical Eigen code twice, one with SSE one > without, and do the platform check in your own app. The good thing > here is that Eigen makes sure that the ABI is independent of whether > vectorization is enabled. > I understand that it is not a goal of eigen, and that should be the application's job. It is just that MKL does it automatically, and doing it in a cross platform way in the context of python extensions is quite hard because of various linking strategies on different OS. cheers, David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
