Quoting Eugene:

> Riunning the JBT optimization on he high end GPU (AMD Radeon 7900 series)
indicates a speedup in the order of 1000 times faster. The big drawback is
that it required a complete re-write of the multiple JBT classes,
essentially flattening the object-oriented model into array processing...



On Thu, Oct 9, 2014 at 5:51 AM, Klaus <[email protected]> wrote:

> Not sure where you take the 1000x improvement from? I was talking about a
> 30x - and this was an ad hoc guess.
>
> If you look around, there are comparisons which make this seem realistic
> (e.g., http://www.hs.uni-hamburg.de/DE/Ins/Per/Abhranil/summer_report.pdf),
> but this also shows it dependents very much on the task specifics. This
> would be the interesting question here, whether such a speed-up would be
> realistic.
> (there the border is at a factor of 400, but this obviously depends on the
> specifics of the CPU and GPU compared)
>
>
> Am Donnerstag, 9. Oktober 2014 11:33:27 UTC+2 schrieb Judson Wilson:
>>
>> I didn't notice this was in the original thread. A factor of 1000x
>> improvement? REALLY? I have a hard time believing it.
>>
>>
>> On Thu, Oct 9, 2014 at 1:18 AM, Klaus <[email protected]> wrote:
>>
>>> Your mileage may vary.
>>> For me, such a solution might mean a speedup of about 30 (given the
>>> number of graphics cores, the differences in performance, non-optimal
>>> parallelization, etc. - but it is a rather wild, but conservative guess).
>>> If you look at pricing on the cloud, using this might cost like 5-10 USD
>>> / hour (were also partial hours count).
>>> Given the number of runs one has to do to reach a reasonable result,
>>> this can easily go to dozens/hundreds of hours. This might be ok from your
>>> perspective, it seems like a bad deal from my point of view. (this is also
>>> why modern high-performance computers often take a GPU heavy approach)
>>> So, I think, going GPU first is probably a good idea, then one might
>>> want to cascade it later with a multicomputer approach (AWS offers specific
>>> GPU instances for the cloud).
>>>
>>> But all this is mute, the question is whether any one with the necessary
>>> expertise is pursuing this approach.
>>> Or perhaps someone has better data for estimating the kinds of results
>>> to be expected.
>>>
>>> Klaus
>>>
>>>
>>>
>>> Am Mittwoch, 8. Oktober 2014 22:14:44 UTC+2 schrieb Judson Wilson:
>>>>
>>>> I think it would be more worthwhile to do a cloud based multi-computer
>>>> approach.
>>>>
>>>> On Wed, Oct 8, 2014 at 3:55 AM, Klaus <[email protected]> wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I was just wondering: was there any further efforts to do CUDA-based
>>>>> backtesting for JBT?
>>>>>
>>>>> Cheers
>>>>>  Klaus
>>>>>
>>>>>
>>>>> Am Donnerstag, 18. April 2013 03:44:58 UTC+2 schrieb Sheb Latsama:
>>>>>
>>>>>> I am looking into this, but it is still far from ready for prime
>>>>>> time.  I have a good speedup, but for some reason the results are 
>>>>>> diverging
>>>>>> from the expected values in seemingly random ways.  I will update the 
>>>>>> group
>>>>>> when I have something ready to share... it is still 'pre-alpha' at this
>>>>>> point.
>>>>>>
>>>>>> I asked Eugene to remove some info I shared with him on this topic as
>>>>>> it is premature at this point, and he was kind enough to do so.
>>>>>>
>>>>>> On Tuesday, April 16, 2013 6:56:40 AM UTC-7, Mick O'Donnell wrote:
>>>>>>>
>>>>>>> Hi all,
>>>>>>>
>>>>>>> Just wondering if anyone has looked at use parallel processing for
>>>>>>> optimizing strategies yet. I've been using the NVIDIA's CUDA library for
>>>>>>> other heavy lifting jobs with good results. There's a project called 
>>>>>>> JCUDA
>>>>>>> which supplies a Runtime API with Java bindings for CUDA. If no one has
>>>>>>> written anything for this yet, I may take it on as a side project.
>>>>>>>
>>>>>>> http://www.jcuda.org/
>>>>>>>
>>>>>>> regards,
>>>>>>>
>>>>>>> Michael
>>>>>>>
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