alt Hacker NewsThey were also full of lies when they have started their GPGPU adventure (like also today).
For a few years they have repeated continuously how GPGPU can provide about 100 times more speed than CPUs.
This has always been false. GPUs are really much faster, but their performance per watt has oscillated during most of the time around 3 times and sometimes up to 4 times greater in comparison with CPUs. This is impressive, but very far from the "100" factor originally claimed by NVIDIA.
Far more annoying than the exaggerated performance claims, is how the NVIDIA CEO was talking during the first GPGPU years about how their GPUs will cause a democratization of computing, giving access for everyone to high-throughput computing.
After a few years, these optimistic prophecies have stopped and NVIDIA has promptly removed FP64 support from their price-acceptable GPUs.
A few years later, AMD has followed the NVIDIA example.
Now, only Intel has made an attempt to revive GPUs as "GPGPUs", but there seems to be little conviction behind this attempt, as they do not even advertise the capabilities of their GPUs. If Intel will also abandon this market, than the "general-purpose" in GPGPUs will really become dead.
Replies
> GPGPU can provide about 100 times more speed than CPUs
Ok. You're talking about performance.
> their performance per watt has oscillated during most of the time around 3 times and sometimes up to 4 times greater in comparison with CPUs
Now you're talking about perf/W.
> This is impressive, but very far from the "100" factor originally claimed by NVIDIA.
That's because you're comparing apples to apples per apple cart.
GPGPU is doing better than ever.
Sure FP64 is a problem and not always available in the capacity people would like it to be, but there are a lot of things you can do just fine with FP32 and all of that research and engineering absolutely is done on GPU.
The AI-craze also made all of it much more accessible. You don't need advanced C++ knowledge anymore to write and run a CUDA project anymore. You can just take Pytorch, JAX, CuPy or whatnot and accelerate your numpy code by an order of magnitude or two. Basically everyone in STEM is using Python these days and the scientific stack works beautifully with nvidia GPUs. Guess which chip maker will benefit if any of that research turns out to be a breakout success in need of more compute?