Konrad Hinsen <konrad.hin...@fastmail.net> writes:

> Thanks for the link! Judging from the example in the README, it's a
> library for task farming in Clojure. While that's a limited form of
> parallelism, there are still lots of applications where it is useful,
> so I'd say this library is definitely worth a closer look. However, it
> doesn't seem to deal with distributed data.

Distributed data is hard, though, partly because kind of distribution
you need depends on your calculation. Every time I've had to do a
distributed calculations, I've always just used the filesystem for data.

I see a lot of frameworks that assume the data is small and can be
entirely contained in the "message," while I need some kind of data
affinity. (I do model estimation on large data sets, so I'd like to send
a lump of data to different nodes, leave it there, then exchange
parameter vectors and error scores with a controller.)

In today's world, I've found I get more done faster with a single 8-core
machine with a lot of RAM (96 GB now; at a previous employer I had
access to a 512 GB monster) than I would with a farm of machines with
only 4 GB or 8 GB, so I'm back to concurrency.  Of course, that's just
because my data is large, but not too large.


>>  I come from the scientific computing community .. the likes of
>> Computation Fluid Dynamics and related topics.. large matrix
>> operations and such stuff..
>
> My background is somewhat similar: molecular simulations and analysis
> of large data sets.

I did astronomy, but mostly small-scale stuff.  Integration, cascade
calculations, the like.  These days, though, I'm doing finance,
mortgages in particular.  That's a field that's been fun for the past
few years.

-Johann

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