https://www.jsoftware.com/help/dictionary/dbdotn.htm

   #~.(i.1024) 32 b. 1
64

Or, on my machine, the bitwise operations give me 64 bits packed in a J atom.

Constructing mechanisms which make an array of such things appear to
be a bit array of arbitrary dimensions should be straightforward for
someone with your background. (Though you could also use them "as is"
with an implicit trailing dimension of 64.)

Thanks,

-- 
Raul

On Mon, Aug 2, 2021 at 11:34 PM greg heil <[email protected]> wrote:
>
> Henry
>
> Hmm, how would one create an array of bits?
> use packed 8-bit characters?
> would one use a similar strategy
> for 16-bit floats?
> (which i am more interested in,
> and is directly supported in most hardware
> -for its utility in Deep Learning)
>
> Seems a serious amount of
> packing unpacking
> indexing chopping and inverting
> just for matrix multiplication
> i could be wrong
> but i do not see the algorithm
>
> ~greg heil
> https//picsrp.github.io
>
> --
>
> from: Henry Rich <[email protected]>
> reply-to: [email protected]
> to: [email protected]
> date: Aug 2, 2021, 8:07 PM
> subject: Re: [Jprogramming] Two implementation questions
>
> >Rather than having a new datatype, you could create arrays of bits and use 
> >bitwise booleans on them.
>
> >Can you list a set of operations on bit arrays that would suffice to solve 
> >the problems you were working on?
>
> Henry Rich
>
> --
>
> from: greg heil <[email protected]>
> to: Programming forum <[email protected]>
> date: Aug 2, 2021, 7:07 PM
> subject: Re: [Jprogramming] Two implementation questions
>
> i did my undergraduate and doctoral thesis
> on the categories of Graphs
> with multiplication (and/or) as the operator
> both were done using APL's bit-boolean's
>
> much more than just multiplication
> were done on these graphs
> homomorphism, isomorphism
> clustering cliquing
> transitive/closures
> all benefited from the simpler
> bit-boolean's
>
> as categories of graphs
> run the full gamut
> from 0-100% full
> one cannot use
> any form of linked list
>
> a size multiplication by 8
> would (at least in those days)
> have caused a LOT of cache misses
>
> even now caching would
> be a lot happier
> because the Social Networks
> under investigation
> (the object of the studies)
> have grown a LOT bigger!-)
>
> ~greg heil
> https//picsrp.github.io
> .
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