psyco

https://www.cnblogs.com/psyco/p/10593205.html Map<String, Integer> items = new HashMap<>(); items.put("A", 10); items.put("B", 20); items.put("C", 30); items.put("D", 40); items.put("E", 50); items.put("F", 60); items.forEach((k,v)->System.out.println("Item : " + k + " Count : " + v)); items.forEach((k,v)->{ System.out.println("Item : " + k + " Count : " + v); if("E".equals(k)){ System.out.println("Hello E"); } }); List<String> items = new ArrayList<>(); items.add("A"); items.add("B"); items.add("C"); items.add("D"); items.add("E"); // lambda // Output : A,B,C,D,E items.forEach(item -> System

Is there a way to get the same sort of speedup on x64 architecture as you can get from psyco on 32 bit processors? mipadi No, unfortunately, Psyco only runs on 32-bit x86 right now . 来源： https://stackoverflow.com/questions/381479/psyco-x64

Apparently Psyco doesn't work for Python 2.7. Are there other alternatives? Today's state of the art in Python compilation is PyPy . I don't have any information about whether it will work for you on your target platform. I think there's no real alternative only using Python as psyco does (Pypy which is increasingly becoming the 'only Python' alternative doesn't support extension modules, so, right now, many of projects can't migrate, but if yours isn't one of those, it can be a nice approach) If you can't use Pypy right now, after doing a profile session and confirming things can't be speed

The Psyco docs say: Just for reference, Psyco does not work on any 64-bit systems at all. This fact is worth being noted again, now that the latest Mac OS/X 10.6 "Snow Leopart" comes with a default Python that is 64-bit on 64-bit machines. The only way to use Psyco on OS/X 10.6 is by recompiling a custom Python in 32-bit mode. In general, porting programs from 32 to 64 bits is only really an issue when the code assumes a certain size for a pointer type and other similarly small(ish) issues. Considering that Psyco isn't a whole lot of code (~32K lines of C + ~8K lines of Python), how hard could

psyco seems to be quite helpful in optimizing Python code, and it does it in a very non-intrusive way. Therefore, one has to wonder. Assuming you're always on a x86 architecture (which is where most apps run these days), why not just always use psyco for all Python code? Does it make mistakes sometimes and ruins the correctness of the program? Increases the runtime for some weird cases? Have you had any negative experiences with it? My most negative experience so far was that it made my code faster by only 15%. Usually it's better. Naturally, using psyco is not a replacement for efficient