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Aren't GPUs a good enough example of hardware that can be programmed to do things way faster than what is possible with CPUs of the same era? Also what about F

by sriku 11y ago

Aren't GPUs a good enough example of hardware that can be programmed to do things way faster than what is possible with CPUs of the same era? Also what about FPGAs? If compiling a specific program down to an FPGA is possible, what kinds of optimizations can we bring in? (I am indeed waving my hands here, but we already have had genetic-proframming optimized FPGAs for over 15 years in research.)

_yosefk 11y ago
Isn't it obvious after all these years that gpus or fpgas give hlls nothing?? Nobody actually runs hlls there, even! And everyone had years and years to try! On these machines in reality hll performance blows much much harder than on cpus and at what point does it become obvious that nobody doing something must be due to reasons that can be learned and understood instead of ignored? I wrote tfa to explain some of those reasons and nobody seems to read it, they just keep saying what they always do. Someone is wrong on the Internet!!
- sklogic 11y ago
  Matlab is quite a HLL, and it benefits a lot from GPUs. LabView is a HLL too, obviously. Not to mention numerous DSLs (also pretty high level) running on GPUs (e.g., I'm running a derivative of the OpenSCAD language on a GPU, for a fast and precise slicing). As HLL as it gets.
- sriku 11y ago
  I didn't state my point clearly. My point was not that GPUs give some new capability to HLLs, but that GLSL itself is higher level for a restricted domain than what is afforded by a general purpose processor ... and, owing to their specialization, GPUs are faster than normal CPUs. FPGAs blur what it means to "run a HLL", since you can optimize the circuitry to a point where there is no explicit representation of the "code" or "microcode" anywhere so the question becomes pointless. Heck even what part of physics is used is blurred! [1,2] [1] https://en.wikipedia.org/wiki/Evolvable_hardware https://en.wikipedia.org/wiki/Evolvable_hardware (Ref: Adrian Thompson's work) [2] http://www.damninteresting.com/on-the-origin-of-circuits/ http://www.damninteresting.com/on-the-origin-of-circuits/
Bognar 11y ago
I don't think a GPU is significantly faster than a CPU, it's just highly parallelized. Many FPGAs are similar, but it's hard to generalize that space. FPGAs and ASICs are also usually designed with a specific task in mind and are not suitable for general computing.
- sriku 11y ago
  That line of reasoning would take the shape of a "no true scotsman". As long as we're talking about alternative hardware architectures that can enable computation speeds otherwise not possible, we ought to include parallel computers, GPUs, FPGAs in the mix. The connection machine doesn't count? Why exclude FPGAs? As long as I can have a program that will reprogram an auxiliary FPGA to do what I want more efficiently, it counts as an alternative processor with a potentially odd architecture where a gate acts as a switch in one program and works as a memory bit in another. They are named "programmable" for a reason.

by sriku 11y ago

_yosefk 11y ago

Isn't it obvious after all these years that gpus or fpgas give hlls nothing?? Nobody actually runs hlls there, even! And everyone had years and years to try! On these machines in reality hll performance blows much much harder than on cpus and at what point does it become obvious that nobody doing something must be due to reasons that can be learned and understood instead of ignored? I wrote tfa to explain some of those reasons and nobody seems to read it, they just keep saying what they always do. Someone is wrong on the Internet!!

sklogic 11y ago
Matlab is quite a HLL, and it benefits a lot from GPUs. LabView is a HLL too, obviously. Not to mention numerous DSLs (also pretty high level) running on GPUs (e.g., I'm running a derivative of the OpenSCAD language on a GPU, for a fast and precise slicing). As HLL as it gets.
sriku 11y ago
I didn't state my point clearly. My point was not that GPUs give some new capability to HLLs, but that GLSL itself is higher level for a restricted domain than what is afforded by a general purpose processor ... and, owing to their specialization, GPUs are faster than normal CPUs. FPGAs blur what it means to "run a HLL", since you can optimize the circuitry to a point where there is no explicit representation of the "code" or "microcode" anywhere so the question becomes pointless. Heck even what part of physics is used is blurred! [1,2] [1] https://en.wikipedia.org/wiki/Evolvable_hardware https://en.wikipedia.org/wiki/Evolvable_hardware (Ref: Adrian Thompson's work) [2] http://www.damninteresting.com/on-the-origin-of-circuits/ http://www.damninteresting.com/on-the-origin-of-circuits/

Bognar 11y ago

I don't think a GPU is significantly faster than a CPU, it's just highly parallelized. Many FPGAs are similar, but it's hard to generalize that space. FPGAs and ASICs are also usually designed with a specific task in mind and are not suitable for general computing.

sriku 11y ago
That line of reasoning would take the shape of a "no true scotsman". As long as we're talking about alternative hardware architectures that can enable computation speeds otherwise not possible, we ought to include parallel computers, GPUs, FPGAs in the mix. The connection machine doesn't count? Why exclude FPGAs? As long as I can have a program that will reprogram an auxiliary FPGA to do what I want more efficiently, it counts as an alternative processor with a potentially odd architecture where a gate acts as a switch in one program and works as a memory bit in another. They are named "programmable" for a reason.