A Dijous 24 Maig 2007 20:33, Francesc Altet escrigué:
Hi,
Some time ago I made an improvement in speed on the numexpr version of PyTables so as to accelerate the operations with unaligned arrays (objects that can appear quite commonly when dealing with columns of recarrays, as PyTables does).
This improvement has demostrated to work correctly and flawlessly in Linux machines (using GCC 4.x and in both 32-bit and 64-bit Linux boxes) for several weeks of intensive testing. Moreover, its speed-up is ranging from a 40% on modern processors and up to a 70% in older ones, so I'd like to keep it.
The surprise came today when I tried to compile the same code on a Windows box (Win XP Service Pack 2) using MSVC 7.1, through the free (as in beer) Toolkit 2003. The compilation process went fine, but the problem is that I'm getting crashes from time to time when running the numexpr test suite.
After some in-depth investigation, I'm pretty sure that the problem is in a concrete part of the code that I'd modified for this improvement. IMO, the affected code is in numexpr/interp_body.c and reads like:
case OP_COPY_II: VEC_ARG1(memcpy(dest, x1, sizeof(int)); dest += sizeof(int); x1 += stride1); case OP_COPY_LL: VEC_ARG1(memcpy(dest, x1, sizeof(long long)); dest += sizeof(long long); x1 += stride1); case OP_COPY_FF: VEC_ARG1(memcpy(dest, x1, sizeof(double)); dest += sizeof(double); x1 += stride1); case OP_COPY_CC: VEC_ARG1(memcpy(dest, x1, sizeof(double)*2); dest += sizeof(double)*2; x1 += stride1); [snip]
Just for the record: I've found the culprit. The problem here was the use of the stride1 variable that was declared just above the main switch for opcodes as: intp stride1 = params.memsteps[arg1]; Unfortunately, this assignment gave problems because arg1 can take values out of the range of memsteps array. The solution was to use another variable, that was initialized as: intp sb1 = params.memsteps[arg1]; but in the VEC_ARG* macros, just after the BOUNDS_CHECK(arg1) call, so that it checks that arg1 doesn't get out of range. All in all, a very subtle bug that would have evident for Numexpr main authors, but not for me. Anyway, you can find the details of the fix in: http://www.pytables.org/trac/changeset/2939 I don't know exactly why, this wasn't giving problems with Linux boxes. Fortunately, Windows platform is much more finicky in terms of memory problems and brought this bug to my attention. Oh, thanks god for letting Windows be! ;) Cheers, --
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