[Blabber] EC2 instances with FPGAs

Connor Connor john.theman.connor at gmail.com
Thu Dec 8 13:38:24 UTC 2016


I just came across these: https://www.liberouter.org/technologies/cards/

Obviously not available on EC2, but I thought they were pretty neat.

On Wed, Nov 30, 2016 at 10:04 PM, Peter S. Shenkin <shenkin at gmail.com> wrote:
> Another thought: One possibility in the number crunching realm is tight
> optimized loops over small arrays, which don't benefit much from GPUs. An
> example is quantum apps. But the Azure article would lead me to think that
> maybe they're not primarily for number-crunching at all.
>
> I don't think interface to special purpose HW is likely to be the point on a
> general-purpose cloud instance. I mean, such an instance could use FPGAs at
> the infrastructure level optimize their HW interfaces, but what is discussed
> in the article is cloud instances that allow the user to program the FPGA.
> Encryption and related is a real possibility. "user-defined algorithm
> support" does not sufficiently differentiate it from GPU; what algorithms
> are better suited for FPGAs would then be the question. GPUs excel on heavy
> number-crunching on large arrays of data, but they've got to be large
> arrays. That's  what led to my thought in the previous paragraph, where GPU
> has not made great inroads.
>
> I guess the primary questions are: (a) Would they be used more for
> number-crunching (as in floating point) or more for "other" types of
> calculation (including integer calculations); (b) to the extent that the
> answer is "Yes", which classes of floating-point algorithm are better suited
> to GPU and which are better suited to FPGA; (c) To the extent that the
> answer is "No", what sorts of computational algorithms are best suited to
> FPGA; in what realm do we get the greatest bang for the buck?
>
> Best,
> -P.
>
> On 30 Nov 2016, at 2:29 PM, John Larson <larsonj.home at gmail.com> wrote:
>
> Peter,
>
> Hre are my thoughts on that:
>
> GPU
> - good for heavy number crunching on large amounts of data
> - very fast and wide memory bank but can require a lot of memory
> - requires PC architecture and power
> - software algorithms on general purpose processors
>
> FPGA -
> - can function like a low power SBA, does not require the hardware and power
> required by GPUs
> - user defined algorithm support that runs directly on the device
> - has customizable io that can be interfaced directly to other specialized
> hardware
> - good at accelerating a broad range of real-time workloads like encryption
> and IP traffic manipulation
>
> Once example that I can think of is how financial institutions use FPGA in
> ultra-low latency systems to optimize network traffic switching and for
> accelerating feed handling and market data workloads.
>
> I think of filtering real-time high-volume network data by complex
> expressions as a general place where they could be useful vs GPU.
>
> Here is a recent article in fortune that discussed how Microsoft is using
> them in the Azure cloud.
> http://fortune.com/2016/10/17/microsoft-fpga-chips-azure/
>
> John
>
> On Wed, Nov 30, 2016 at 1:11 PM, Peter S. Shenkin <shenkin at gmail.com> wrote:
>>
>>
>> On 30 Nov 2016, at 1:07 PM, Guan Yang <guan at yang.dk> wrote:
>>
>>
>> https://aws.amazon.com/blogs/aws/developer-preview-ec2-instances-f1-with-programmable-hardware/
>>
>>
>> Interesting.
>>
>> Does anyone know what kinds of apps work better with FPGA co-processors
>> than with GPUs, which are basically vector processors that can run
>> trigonometric functions very quickly?
>>
>> -P.
>>
>>
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