Easy & huge GC optimizations

[Chris via Digitalmars-d]

On Friday, 23 May 2014 at 06:17:43 UTC, Rainer Schuetze wrote:
>
>
> On 22.05.2014 21:04, Etienne wrote:
>> On 2014-05-22 2:12 PM, Rainer Schuetze wrote:
>>>
>>> "NO_INTERIOR" is currently only used for the hash array used 
>>> by
>>> associative arrays. It is a bit dangerous to use as any 
>>> pointer,slice or
>>> register still operating on the array is ignored, so 
>>> collecting it might
>>> corrupt your memory.
>>
>> That's quite a relief, I was afraid of having to do it ;)
>>
>> I'm currently exploring the possibility of sampling the 
>> pointers during
>> mark'ing to check if they're gone and using bayesian 
>> probabilities to
>> decide whether or not to skip the pool.
>>
>> I explained it all here:
>> https://github.com/D-Programming-Language/druntime/pull/797#issuecomment-43896016
>>
>>
>> -- paste --
>> Basically, when marking, you take 1 in X of the references and 
>> send them
>> to a specific array that represents the pool they refer to. 
>> Then, next
>> time you're going to collect you test them individually and if 
>> they're
>> mostly there, you skip marking/free'ing for that particular 
>> pool during
>> collection. You can force collection on certain pools every 1 
>> in X
>> collections to even out the average lifetime of the references.
>>
>> You're going to want to have a lower certainty of failure for 
>> big
>> allocations, but basically you're using probabilities to avoid 
>> pushing a
>> lot of useless load on the processor, especially when you're 
>> in a part
>> of an application that's just allocating a lot (sampling will 
>> determine
>> that the software is not in a state of data removal).
>>
>> http://en.wikipedia.org/wiki/Bayes_factor
>>
>> -- end paste --
>>
>> The bayes factor is merely there to choose the appropriate 
>> model that
>> fits with the program. Bayesian inference would take care of 
>> deciding if
>> a pool should end up being mark'ed. In other words, machine 
>> learning.
>>
>> Would you think it'd be a good optimization opportunity?
>
> Hmm, I guess I don't get the idea. You cannot skip a pool based 
> on some statistics, you might have references in there to 
> anything. As a result you cannot collect anything.

I'm not a fan of machine learning, especially not in cases you 
_can_ control, like memory allocation / deallocation. Guessing is 
not a good strategy, if you can have control over something. 
Machine learning is only good for vast and unpredictable data 
(voice recognition for example). Then it makes sense to apply 
probability. But if you can actually control what you are doing, 
why would you want to rely on a stupid and blind machine that 
decides things for you based on a probability of n%? Things can 
go wrong and you don't even know why. Mind you, we should rule 
the machines, not the other way around.