What led to the transition from CPU-centric computing to very heavily GPU-centric starting in the 2010s? Is there a necessity for CPU computing whatsoever aside from the sheer standardized nature of CPU architectures? The advent of GPGPU computing might have one believe that the usefulness of CPUs are limited to very few tasks - and that's only due to the inability of programmers to fully utilize GPU architectures. Am I missing something? What even is the point of a CPU anymore?
GPU/CPU Computing
Jose Ward
Jackson Nguyen
The computing is still CPU-centric.
Nolan Bailey
The bitcoin fever sueely had something to do with this trend. That and vydia ofc
Joshua Anderson
Undoubtedly - that and server-side rendering forwarded to what are effectively graphical dumb terminals. An interesting paradigm, but why even have a CPU? Seems like it's going to soon become nothing more than a fallback when the GPU fails, particularly now that Parallel Studio exists and what have you.
Bentley Long
> What led to the transition from CPU-centric computing to very heavily GPU-centric starting in the 2010s?
Floating point requirements, primarily.
> Is there a necessity for CPU computing whatsoever aside from the sheer standardized nature of CPU architectures?
Sure. GPU's are still ASIC's, where CPU's have a more general use.
> What even is the point of a CPU anymore?
Something still has to feed the GPU's. And there's I/O (memory, storage, network), as well as all the general purpose stuff a computer requires.
Mining rigs are a good example of this though. 6+ GPU's on a system, with at most an i3 and 8GB RAM. The CPU doesn't do much, but it still runs the OS and I/O.
Blake Ramirez
>All CPU computing
Running
>CPU + GPU
Driving a car
>GPU only
Launching a car at full speed towards the destination without being on the car
Jordan James
t. never written an opencl program
Eli Howard
Some problems simply benefit from having a fuckload of low power, highly parallelised cores with a low amount of memory thrown at them.
GPUs happen to have a fuckload of low power, highly parallelised cores with a low amount of memory.
Christopher Phillips
if you want to turn a planck into sawdust you do it at home with a blender in 3 minutes
if you want to turn a truckload of wood into sawdust you take an hour to drive to the lumbermill, get it done in 30 minutes, then drive back
you wouldnt drive there for one small plank
you wouldnt try to process a truckload of wood with a blender
Jordan Hall
Gpu computing is very limited in use. Cuda is the only real viable platform, openCL is a toy and a poor knockoff