Hi all,
A possible way to come around portability and other issues, you could pack just 
the computationally expensive code into a "slim" library that implements serial 
code or GPU code if the hardware is available.
In past projects I noted that multi-core teams and GPU teams produce both good 
solutions if they work competitively and cooperatively on the same algorithms.

Francisco
 
On 23.08.2013, at 22:57, Tim McNamara wrote:

> On 24 August 2013 08:25, Oreste Villa <[email protected]> wrote:
> Tim,
> 
> I partially agree with you, in the sense that if you are not careful 
> portability can be an issue. Having said that I have the following comments.
> 
> 1) We are talking about a small prototype for the CLA algorithm on GPU (used 
> to understand potential benefits of the approach) and not moving NuPIC 
> tomorrow to GPU X.
> 2) I plan to make several tests (starting with CUDA) but ultimately using 
> OpenCL or openACC which support multiple architectures (multi-core CPUs and 
> GPU of different vendors).
> 3) I would really like to work on MPI on the parallelism at larger scale, but 
> I have very limited bandwidth and unfortunately I can't work on both now (I 
> am glad to help and advise if there people that want to try immediately)
> 
> Oreste
>  
> I don't want to get in the way of good work! CUDA & OpenCL are very effective 
> technologies and I am actually very supportive. If you are happy to invest 
> your time, then that seems like a great way forward. OpenACC directives could 
> be very nice to include, as they really are device independent.
> 
> My concerns are:
> 
>  - decrease portability, as mentioned earlier
>  - increasing the barrier to entry to get up and running, e.g. build the 
> system, users will have more documentation to read and compiler flags to learn
>  - code readability, more tokens makes code harder to grok  
> 
> It sounds like you have a lot of enthusiasm though. Your work is likely to 
> benefit the whole community. Good luck :)
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