On Tue, Jun 19, 2018 at 04:47:46PM +0200, Martin Bammer wrote:
> And here comes the idea for POPT. With this idea the Python interpreter has
> running several threads in background (1 thread for each object type)
The builtins alone has 47 exception types. I hope you don't mean that
each of those gets its own thread.
Then there are builtins:
str, bytes, int, float, set, frozenset, list, tuple, dict,
type, bytearray, property, staticmethod, classmethod,
range objects, zip objects, filter objects, slice objects,
map objects, MethodType, FunctionType, ModuleType
and probably more I forgot. That's 20 threads there. Don't forget things
like code objects, DictProxies, BuiltinMethodOrFunction, etc.
When I run threaded code on my computer, I find that about 6 or 8
threads is optimal, and more than that and the code slows down. You want
to use 25-30 threads just for memory management. Why do you hate me?
*wink*
> which
> manage a set of objects as an object cache. Each object in the cache is
> already preconfigured by the object provider thread. So only the part of
> the object structure which is individual has to be initialized.
For many objects, wouldn't that be close enough to "all of it"? (Apart
from a couple of fields which never change.)
> This saves a lot of processing time for the main thread
Do you know this for a fact or are you just hoping?
> and the memory management has
> much less to do, because temporarily unused objects can be reused
> immediately.
Or, unused objects can sit around for a long, long time, locking up
memory in a cache that would be better allocated towards *used* objects.
> Another advantage is that every Python code uses several CPU cores in
> parallel, even if it is a single threaded application, without the need to
> change the Python code.
How do you synchronise these threaded calls? Suppose I write this:
x = (3.5, 4.5, "a", "b", {}, [], 1234567890, b"abcd", "c", 5.5)
That has to syncronise 10 pieces of output from six threads before it
can construct the tuple. How much overhead does that have?
> If this idea is well implemented I expect a big performance improvement for
> all Python applications.
What are your reasons for this expectation? Do other interpreters do
this? Have you tried an implementation and got promising results?
--
Steve
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