I wish people would stop quoting marketing material as some kind representation of what they know.

You're giving completely the wrong impression about dsp slices - it is absolutely not 1 dsp slice per FP operator at any precision that you would want to do floating point arithmetic. It's definitely at least 2 plus a whole bunch of LUTs (~500) for FP16 with 4 stages or something like that. And if you want faster (fewer stages) then you need more slices. On alveo u280, which is an ultrascale part, I have never been able to effectively utilize more than ~4000 dsp slices (out of 9024) for 5,4 mults and that cost basically 99% of clbs in SLR1 and SLR2.

And even then, disconnected FPUs are completely meaningless without a datapath implementing eg matmul and boy oh boy do you have no clue what you're in for there.

Takeaway: it's pointless to compare raw specsheet numbers when everything comes down to datapath.

> everything comes down to datapath.

Ain't that the truth.

However: Xilinx's DSP58 blocks (Versal devices), and older Intel DSPs, do integrate floating point into the DSP tiles - which does narrow the gap between the datasheet's DSP count and the number of floating-point operations achievable per clock.

> Xilinx's DSP58 blocks (Versal devices)

this is correct (i am currently working on designs that target these parts) but those things are more expensive than comparably sized/equipped nvidia gpus so (in the context of this discussion) it's moot.

Ballas mentions in a neighboring reply that the eGPU from the paper is using an Agilex 7: https://ark.intel.com/content/www/us/en/ark/products/217645/...

Yes but as you can see by reading, this comment chain starts with someone linking to a xilinx product

The Intel Agilex (that was used in this paper) DSP blocks natively support single and half precision floating point.

The FPGAs with enough multipliers to be competitive against an actual GPU are going to be quite a bit more expensive than a GPU aren't they?

How much would that FPGA cost?

And my 6-year-old Consumer GPU (Vega64) has 4096 32-bit multipliers at 1500 MHz clock. (And I bought it a year or two late, so it was only $400). With 8GB HBM2 RAM at 512GBps (yes, Gigabytes per second) throughput. You absolutely are not getting anything close to a consumer GPU in terms of performance or cost on any other platform.

---------

FPGAs advantage is that systolic array arrangement. I'm pretty sure GPUs will win in a "hardware multiplier" war, but it might be harder to get all the data into a typical GPU

There's a fair number of programs that will never utilize a GPU with any level of efficiency. That's where FPGAs come in, utilization will be better because of LUTs and Routers and all that magic.

But if people are looking for a TFLOP fight with raw numbers of multipliers? My bet is on the GPU and SIMD-processing in general. Those architectures are just crazy.

--------------

BTW: That top end DSP slice is documented here: https://docs.xilinx.com/v/u/en-US/ug579-ultrascale-dsp

That's a 27-bit x 18-bit multiplier per dsp.

In contrast, a 32-bit floating point operation is 24-bit x 24-bit multiplier plus a bit of extra stuff for the exponent bits. I'm pretty sure that the 4096-shader Vega64 actually had 32-bit multipliers but I'm not 100% sure on that.

But the float 24-bit x 24-bit multiplier would need more than one dsp to replicate, maybe two of them. AMD's newest GPUs are 6144-shader... but those shaders can execute _TWO_ multiplies per clock tick for a total of 12288 hardware multipliers.