Re: Using the GPU

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От
Alejandro Torras
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Re: Using the GPU
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4670722E.6030005@yahoo.es
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Re: Using the GPU (Alejandro Torras)
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Using the GPU "Billings, John" <John.Billings@PAETEC.com>
Re: Using the GPU Alejandro Torras <atec_post@yahoo.es>
Re: Using the GPU Alejandro Torras <atec_post@yahoo.es>
Re: Using the GPU "Dawid Kuroczko" <qnex42@gmail.com>
Re: Using the GPU "Alexander Staubo" <alex@purefiction.net>
Re: Using the GPU Alban Hertroys <alban@magproductions.nl>
Re: Using the GPU Tom Allison <tom@tacocat.net>
Re: Using the GPU "Alexander Staubo" <alex@purefiction.net>
Re: Using the GPU Tom Lane <tgl@sss.pgh.pa.us>
Re: Using the GPU Tom Allison <tom@tacocat.net>
Re: Using the GPU Gregory Stark <stark@enterprisedb.com>
Re: Using the GPU "Alexander Staubo" <alex@purefiction.net>
Alejandro Torras wrote:
> Billings, John wrote:
>> Does anyone think that PostgreSQL could benefit from using the video 
>> card as a parallel computing device?  I'm working on a project using 
>> Nvidia's CUDA with an 8800 series video card to handle non-graphical 
>> algorithms.  I'm curious if anyone thinks that this technology could 
>> be used to speed up a database?  If so which part of the database, 
>> and what kind of parallel algorithms would be used?
>>  
>
> Looking at nvidia's cuda homepage 
> (http://developer.nvidia.com/object/cuda.html), I see that the 
> parallel bitonic sorting could be used instead of 
> qsort/heapsort/mergesort (I don't know which is used)
>
I think that the function cublasIsamax() explained at 
http://developer.download.nvidia.com/compute/cuda/0_8/NVIDIA_CUBLAS_Library_0.8.pdf 
can be used to find the maximum of a single precision vector, but 
according with a previous post of Alexander Staubo, this function is 
best suited for fixed-length tuple values.

But could the data be separated into two zones, one for varying-length 
data and other for fixed-length data?
With this approach fixed-length data may be susceptible for more and 
deeper optimizations like parallelization processing.

-- 
Alejandro Torras

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