On Apr 07, 2009 at 02:10AM, Steven D'Aprano <st...@pearwood.info> wrote:
> On the other hand, I'm with Guido when he wrote "it is certainly not > right to choose speed over correctness". This is especially a problem > for floating point optimizations, and I urge Cesare to be conservative > in any f.p. optimizations he introduces, including constant folding. The principle that I followed on doing constant folding was: "do what Python will do without constant folding enabled". So if Python will generate LOAD_CONST 1 LOAD_CONST 2 BINARY_ADD the constant folding code will simply replace them with a single LOAD_CONST 3 When working with such kind of optimizations, the temptation is to apply them at any situation possible. For example, in other languages this a = b * 2 * 3 will be replaced by a = b * 6 In Python I can't do that, because b can be an object which overloaded the * operator, so it *must* be called two times, one for 2 and one for 3. That's the way I choose to implement constant folding. The only difference at this time is regards invalid operations, which will raise exceptions at compile time, not at running time. So if you write: a = 1 / 0 an exception will be raised at compile time. I decided to let the exception be raised immediately, because I think that it's better to detect an error at compile time than at execution time. However, this can leed to incompatibilities with existing code, so in the final implementation I will add a flag to struct compiling (in ast.c) so that this behaviour can be controlled programmatically (enabling or not the exception raising). I already introduced a flag in struct compiling to control the constant folding, that can be completely disabled, if desired. > So... +1 on the general principle of constant folding, -0.5 on any such > optimizations which change the semantics of a f.p. operation. The only > reason it's -0.5 rather than -1 is that (presumably) anyone who cares > about floating point correctness already knows to never trust the > compiler. As Raymond stated, there's no loss in precision working with constant folding code on float datas. That's because there will be a rounding and a store of computed values each time that a result is calculated. Other languages will use FPU registers to hold results as long as possibile, keeping full 80 bit precision (16 bit exponent + 64 bit mantissa). That's not the Python case. Cesare _______________________________________________ Python-Dev mailing list Python-Dev@python.org http://mail.python.org/mailman/listinfo/python-dev Unsubscribe: http://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
