As you mentioned, functions of core.matrix are well-defined.
So openCL may be suitable.
(call native functions by JNA)

(ereduce some-reduce-function (emap some-mapping-function some-gpu-array))

This is wonderful.
Since GPU is appropriate for "map/reduce operations", I think there is a 
room for improvement!

2014年8月7日木曜日 2時46分20秒 UTC+9 Mike Anderson:
>
> Sounds like a great project, would be really great to have a core.matrix 
> implementation running on the GPU!
>
> I'm not too familiar with aparapi. Since core.matrix uses fairly 
> well-defined operations, it might be simpler to code the key ones directly 
> in OpenCL rather than going via Java bytecode translation. 
>
> I think the bytecode translation is interesting however if you want to run 
> arbitrary Clojure code on the GPU. If it works well enough, you might even 
> be able to do things like:
>
> (ereduce some-reduce-function (emap some-mapping-function some-gpu-array))
>
> That would be pretty impressive!
>
> There has also been some discussion on GPU / LLVM related topics on the 
> numerical Clojure group:
> https://groups.google.com/forum/#!forum/numerical-clojure
>
>
> On Tuesday, 5 August 2014 16:22:17 UTC+8, Miki Nobuhiro wrote:
>>
>> Hello, everyone.
>> I want to calculate matrix operations by GPU compuring,
>> and here is my code.
>>
>> https://github.com/bobuhiro11/aparapi-matrix
>> https://clojars.org/aparapi-matrix
>>
>> Now, I have some questions.
>>
>> 1.
>> aparapi-matrix uses Aparapi(https://code.google.com/p/aparapi/).
>> Is this choice correct?
>> A top priority is not to waste user's precious time.
>>
>> 2.
>> I cannot find a repository for Aparapi, so users have to download by hand.
>> Could you tell me better ways?
>>
>>

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