Fast forward several years, and the cryptocurrency craze drove up GPU prices for many years without even touching the floating-point capabilities. Now, FP64 is out because of ML, a field that's almost unrecognizable compared to where it was during the first few years of CUDA's existence.

NVIDIA has been very lucky over the course of their history, but have also done a great job of reacting to new workloads and use cases. But those shifts have definitely created some awkward moments where their existing strategies and roadmaps have been upturned.

With the method from the article, the exponent range remains the same as in single precision, instead of being increased to that of double precision.

There are a lot of applications for which such an exponent range would cause far too frequent overflows and underflows. This could be avoided by introducing a lot of carefully-chosen scaling factors in all formulae, but this tedious work would remove the main advantage of floating-point arithmetic, i.e. the reason why computations are not done in fixed-point.

The general solution of this problem is to emulate double-precision with 3 numbers, 2 FP32 for the significand and a third number for the exponent, either a FP number or an integer number, depending on which format is more convenient for a given GPU.

This is possible, but it lowers considerably the achievable ratio between emulated FP64 throughput and hardware FP32 throughput, but the ratio is still better than the vendor-enforced 1:64 ratio.

Nevertheless, for now any small business or individual user can achieve a much better performance per dollar for FP64 throughput by buying Intel Battlemage GPUs, which have a 1:8 FP64/FP32 throughput ratio. This is much better than you can achieve by emulating FP64 on NVIDIA or AMD GPUs.

Intel B580 is a small GPU, so it has only a FP64 throughput about equal to a Ryzen 9 9900X and smaller than a Ryzen 9 9950X. However it provides that throughput at a much lower price. Thus if you start with a PC with a 9900X/9950X, you can double or almost double the FP64 throughput for a low additional price with an Intel GPU. Multiple GPUs will proportionally multiply the throughput.

The sad part is that with the current Intel CEO and with NVIDIA being a shareholder of Intel, it is unclear whether Intel will continue to compete in the GPU market, or they will abandon it, leaving us at the mercy of NVIDIA and AMD, which both refuse to provide products with good FP64 support to small businesses and individual users.

https://www.eatyourbytes.com/list-of-gpus-by-processing-powe...

Let's say X=10% of the GPU area (~75mm^2) is dedicated to FP32 SIMD units. Assume FP64 units are ~2-4x bigger. That would be 150-300mm^2, a huge amount of area that would increase the price per GPU. You may not agree with these assumptions. Feel free to change them. It is an overhead that is replicated per core. Why would gamers want to pay for any features they don't use?

Not to say there isn't market segmentation going on, but FP64 cost is higher for massively parallel processors than it was in the days of high frequency single core CPUs.

Past a certain threshold of FP64 throughput, your chip goes in a separate category and is subject to more regulation about who you can sell to and know-your-customer. FP32 does not matter for this threshold.

https://en.wikipedia.org/wiki/Adjusted_Peak_Performance

It is not a market segmentation tactic and has been around since 2006. It's part of the mind-numbing annual export control training I get to take.

weird way to frame delivering exactly what the consumer wants as a big market segmentation fuck the user conspiracy