Answering my own question, my grouping example failed because of the 
laziness of the expressions being computed in the pmap: the threads were 
not evaluating the expressions until they were evaluated (sequentially) by 
the main thread. Adding a doall gives the result I was hoping for.

On Sunday, October 13, 2013 4:10:16 PM UTC-7, Brian Craft wrote:
>
> I'm walking a seq of many millions of floats, encoding them for the 
> persistence layer, and getting sequence ids from the db. So, conceptually 
> there are two parts: the slow part, and the side-effecting part. Vaguely 
> like
>
> (map get-ids (map encode float-seq))
>
> which is later reduced while writing to disk. In the get-ids step the 
> order matters. My first attempt to make the slow part parallel was to use 
> pmap,
>
> (map get-ids (pmap encode float-seq))
>
> However that's actually slower. I expect this is because even though 
> "encode" is the bottleneck, it's still faster than the overhead of pmap. I 
> next tried pmap over groups of floats, a bit like
>
> (map get-ids (flatten (pmap #(map encode %) (partition-all 20000 
> float-seq))))
>
> (sorry for any typos, I'm just pseudo-coding here) This was still slower, 
> which surprised me. I understand the first pmap result, but this one is 
> puzzling to me. Even if I partition half the length of the seq (so in 
> theory it can run two threads, each of which will run five or six seconds), 
> it's no faster than map.  Part of this seems to be the overhead of 
> creating more intermediate seqs. Perhaps I'm misunderstanding what's 
> happening during partition-all.
>
> Is there some obvious way to approach this scenario? I looked briefly at 
> the reducers library, however it was unclear to me how to deal with the 
> side-effecting portion of the operation. The second (fast) map operation 
> needs to be done in order.
>

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