# \[ANN\] Vahana.jl - Framework for large-scale agent-based models

**URL:** <https://discourse.julialang.org/t/ann-vahana-jl-framework-for-large-scale-agent-based-models/102024>\
**Category:** Package Announcements\
**Tags:** mpi, agents, graphs, abm, network\
**Created:** [July 24, 2023, 3:00pm UTC](https://discourse.julialang.org/t/ann-vahana-jl-framework-for-large-scale-agent-based-models/102024 "2023-07-24T15:00:06Z")\
**Posts on this page:** 1\
**Showing post:** 7

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**Author:** ![Tortar](https://avatars.discourse-cdn.com/v4/letter/t/6bbea6/32.png) [@Tortar](https://discourse.julialang.org/u/Tortar)\
**Post date:** [July 25, 2023, 11:17am UTC](https://discourse.julialang.org/t/ann-vahana-jl-framework-for-large-scale-agent-based-models/102024/7 "2023-07-25T11:17:28Z")

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The framework looks very interesting! thank you @sfuerst

> [@sfuerst](#):
>
> Yes, of course. But e.g. even for the “small” Berlin scenario of MATSim Episim the memory usage allowed to start only 32 simulations on a node with 384 GB Ram and 96 cores, so 1/3 of the node was not used. And for the simulation of all of Germany, the entire memory is used by one simulation.

indeed I think that the memory usage is the crux of the problem when running multiple simulations at once (for very memory-intensive models). From what I understand though, if you compare independent runs in parallel vs. one run in parallel, it seems reasonable to say that running simulations independently should be actually **faster** not slower (if you can utilize all cores in both situations), simply because there is no between-cores communication overhead.

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