On 21.09.2017 07:24, flxvctr wrote:
> 1. I've tried mcmc_args={'sequential': False, 'parallel': True}, but
> this did not seem to have a great impact regarding speed on my smaller
> k-cores, could it have on bigger ones?
It should give you a 2-3x speedup, but not much more, as part of the
algorithm (the actual node updates) has to be serial.
> 2. What does the warning here
> (https://graph-tool.skewed.de/static/doc/inference.html#graph_tool.inference.BlockState.mcmc_sweep)
> regarding parallel sampling mean in worst case and when is it likely that
> the warning actually applies?
I don't know, this is why the warning is there. I generally advise against
using the parallel code, since it does not make things that much faster to
begin with. I'm even considering removing it in a future version.
> 3. I am considering either to try to fund a machine with more
> (expensive) RAM or to just swap memory on freely available disk space. What
> would you expact would be the impact on speed? Checking the behaviour of the
> algorithm so far I don't see it being I/O bound but rather by available
> memory and the sequential parts of the algo that are running on one core
> only. Is swapping a considerable option or should I just straight away rent
> a bigger machine?
The memory access during the algorithm is random, so using swap will
probably be prohibitively slow.
> 4. Is there any rule of thumb for epsilon? And what is its impact if it's
> too large?
If it is too large, your estimate of the ground state (the best hierarchical
partition) will be worse. Simply, it gives you a quality/speed trade-off.
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
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