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Would AB really run any faster on this PC, or would one have to design
some kind of hack that allowed AB to take advantage of the extra
processors?
My limited understanding was that to get AB's backtesting to run on
NVIDIA graphics cards, some kind of CUDA kernal had to be designed -
and that was a huge job that nobody took on. There was a thread about
that a few months back.
On the other hand, if NVIDIA somehow designed the box so that ALL
software running on it sees the 940 cores as a single, giant
processing core, then that could be very intriguing indeed!
Someone please buy one of these Teslas and report back to the group.
It will only set you back $10,000, but it's for a good cause. ;-)
--- In amibroker@xxxxxxxxxxxxxxx, "Claudiu Bistriceanu" <gunoicb@xxx>
wrote:
>
> Hi guys,
>
> check this out: http://www.nvidia.co.uk/page/personal_computing.html
>
> Nvidia claims that they created the real home supercomputer for a
> price under $10,000.
> "One Researcher,One Supercomputer"
> "Experience cluster level computing performance?up to 250 times faster
> than standard PCs and workstations?right at your desk. The NVIDIA®
> Tesla? Personal Supercomputer is based on the revolutionary NVIDIA®
> CUDA? parallel computing architecture and powered by up to 960
> parallel processing cores."
>
> "Your own Supercomputer
> Dedicated computing resource for every computational researcher and
> technical professional.
>
> Cluster Performance on your Desktop
> The performance of a cluster in a desktop system. Four Tesla GPU
> computing processors deliver close to 4 Tera flops of performance.
>
> Designed for Office Use
> Power-efficient and quiet desktop unit designed to plug into standard
> office power sockets and quiet enough for use at your desk.
>
> Massively Parallel Many Core GPU Architecture
> 240 parallel processor cores per GPU that can execute thousands of
> concurrent threads.
>
> Solve Large-scale Problems using Multiple GPUs
> Scale your application to multiple GPUs and harness the performance of
> thousands of processor cores to solve large-scale problems.
>
> Widely accepted, easy to learn CUDA C Programming Environment
> Easily express application parallelism to take advantage of the GPU's
> many-core architecture using the NVIDIA® CUDA? C programming
environment.
>
> 4 GB High-Speed Memory per GPU
> Enables larger data sets to be stored locally for each processor to
> maximize benefit from the 102GB/s memory transfer speeds and minimize
> data movement around the system.
>
> IEEE 754 Floating Point Precision(single-precision and double-precision)
> Provides results that are consistent across platforms and meet
> industry standards.
>
> 64-bit ALUs for Double-Precision Math
> Meets the precision requirements of your most demanding applications
> with 64-bit ALUs."
>
> running the AB's optimization and backtesting will take just a second!
> :))))
>
> Take care
>
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