Very brief answer :)

What about parpool initialization. Because of the fact, that the main part of MCT is implemented as MEX file, there is probably impossible to use Threads based parpool, becase of MATLAB limitation to run mex file in parallel only via Processes based parpool. Am I right?

Any comprehensive tutorial how to run properly parallel computing with Parallel Toolbox functionality together with MCT would very helpful! 

This answer: "same code as MATLAB's but running in extended precision" is not always absolute true. There are often some minor, but very important differences. But OK ... I understand your point :) 

Any specific user guides, function reference or other documentation regarding MATLAB Parallel Toolbox (parfor, parpool specs like "threads" vs 'processes', etc.) and MCT licensing requirements/options? Are there some limitations, recommended use case scenarios, etc?  

OK... So, the primary source of mp.??? functions documentation is available only via: help mp.???

As is already mentioned in your web documentation:

"Information on toolbox-specific routines can be obtained by the MATLAB help command: help mp.Digits, help mp.GaussLegendre, etc.)"

I think, that all these informations should be available at actual state on your WEB as a part of official documentation. From my point of view is help based documentation very brief, but still sufficient. Problem is, when MCT user trying to show MCT based code to anybody who has not already installed MCT. In this case is web based documentation only available source which is nearly useless, because does not cover this important part of MCT functionality.

Any response? Is this forum still alive?

Just a perfect, Pavel!!! Thanks...:)
Final request: Please add a description of the mp.RandState function to the Web function reference as a basic MCT functions info source.

Thanks again ...

This is a hard question, because for a regular MCT user like me, it is not clear how difficult it is to create fully functional mp.ResetRandom function as an full-precision alternative to the "rng" function. Moreover, full-precision random generators (rand(n) + rng ... 'mp' functions) are so specific, that it is not clear how important these functions really are for other MCT users in general.

From my point of view, is the new "mp.ResetRandom" function optimal solution, I just prefer better name: like "mp.rng" (if is it possible, of course, due to the mentioned overloading problem), to be close to standard MATLAB as possible. I am working now on the full-precision random sampling, where is this topic crucial, including the 'mp' alternative of "rng" to get full control over random generation process.

Especially for me, it is important to have full control over random number generation.

Could you add some clear and simple example how "rng" works with rand(x,'mp') now? Or better, could you describe this specific behavior at function reference WWW page?

And finally, what about rng('default') and rng('shuffle'), these two options are extremely useful? 

The "rng" still not working with rand(1,'mp'):

>> mp.Digits(100)

ans =

300

>> rng(1)
rmp = rand(1,'mp')
rng(1)
rmp = rand(1,'mp')

rmp =

0.1019434353836572895054626158415646843010934355718609316510899011279499964199600542084573004435709827


rmp =

0.7686462842760086723268602992488197377426292102713405281965513370345202336516897460693092237330618525

From my point of view, the following two commands are now fully equivalent: mp(rand(1)) and rand(1,'mp') and final random number is effectively the same, and in double precision.

So I think, that rand(1,'mp') should represent random number in full precision defined by mp.Digits(100) by default, and be equivalent to your experimental function mprand(n).

But in the 'mp' (full precision) case should be possible to control random generator by rng command, too.

So, the suggested way is elimination of mprand(n) at all, and use the rand(1,'mp') at full precision
regime.