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It is very important to emphasize that GPU's only "achieve high performance" in workloads tailored very specifically to their extremely limited architecture. C

It is very important to emphasize that GPU's only "achieve high performance" in workloads tailored very specifically to their extremely limited architecture. CPU's, on the other hand, are designed to be much more generic with decent performance for any task.

derefr 9y ago
I wouldn't say this is precisely true. Look at Dolphin's "ubershaders" (https://dolphin-emu.org/blog/2017/07/30/ubershaders/ https://dolphin-emu.org/blog/2017/07/30/ubershaders/): they're essentially a Turing machine, running on your GPU, used to emulate a GPU of another architecture... and yet this is still (much!) faster than doing the same on the CPU. And there's nothing special about emulating a GPU on a GPU; you could emulate a CPU architecture just as easily, at a much higher level than you get from an FPGA, and so perhaps faster than you'd be able to get from today's FPGAs. And, if you're mapping GPU shader units 1:1 to VM schedulers, you'd also get a far higher degree of core parallelism than even a Xeon Phi-like architecture would give you. (The big limitation is that you'd be very limited in I/O bandwidth out to main memory; but each shader unit would be able to reserve its own small amount of VRAM texture space—i.e. NUMA memory—to work with.) I'm still waiting for someone to port Erlang's BEAM VM to run on a GPU; it'd be a perfect fit. :)
umanwizard 9y ago
I think it's more accurate to say that CPUs are high-performance for different tasks than GPUs. Simple code, very wide data workloads are atrociously slow on CPUs, and single-threaded heavily branching workloads are atrociously slow on GPUs. That doesn't mean that one is more limited than the other.
- arghwhat 9y ago
  I would argue that the total set of "good performance" workloads are smaller on a GPU than they are on a CPU. However, "good performance" on a CPU is much much worse than "good performance" on a GPU. CPU's just achieve their mediocre performance on a larger set of usecases. GPU's are specialized devices that are very good at specialized activities.
- hexane360 9y ago
  The whole point of the article is that this is largely a myth imposed by the memory model of C and C-like languages. Single-threaded heavily branching workloads would be atrociously slow on modern CPUs, if not for branch prediction (which caused Spectre). On modern processors, you have 180 instructions running in one thread. The processor is doing an ok job filling these instructions, but a language and compiler can do a much better job. C doesn't collect any information about data dependencies, and instead just pretends all instructions are sequential. Even code which contains tight loops of sequential commands can be optimized, because you have an entire program and operating system running around that sequential code.

derefr 9y ago

I wouldn't say this is precisely true. Look at Dolphin's "ubershaders" (https://dolphin-emu.org/blog/2017/07/30/ubershaders/ https://dolphin-emu.org/blog/2017/07/30/ubershaders/): they're essentially a Turing machine, running on your GPU, used to emulate a GPU of another architecture... and yet this is still (much!) faster than doing the same on the CPU. And there's nothing special about emulating a GPU on a GPU; you could emulate a CPU architecture just as easily, at a much higher level than you get from an FPGA, and so perhaps faster than you'd be able to get from today's FPGAs. And, if you're mapping GPU shader units 1:1 to VM schedulers, you'd also get a far higher degree of core parallelism than even a Xeon Phi-like architecture would give you. (The big limitation is that you'd be very limited in I/O bandwidth out to main memory; but each shader unit would be able to reserve its own small amount of VRAM texture space—i.e. NUMA memory—to work with.) I'm still waiting for someone to port Erlang's BEAM VM to run on a GPU; it'd be a perfect fit. :)

umanwizard 9y ago

I think it's more accurate to say that CPUs are high-performance for different tasks than GPUs. Simple code, very wide data workloads are atrociously slow on CPUs, and single-threaded heavily branching workloads are atrociously slow on GPUs. That doesn't mean that one is more limited than the other.

arghwhat 9y ago
I would argue that the total set of "good performance" workloads are smaller on a GPU than they are on a CPU. However, "good performance" on a CPU is much much worse than "good performance" on a GPU. CPU's just achieve their mediocre performance on a larger set of usecases. GPU's are specialized devices that are very good at specialized activities.
hexane360 9y ago
The whole point of the article is that this is largely a myth imposed by the memory model of C and C-like languages. Single-threaded heavily branching workloads would be atrociously slow on modern CPUs, if not for branch prediction (which caused Spectre). On modern processors, you have 180 instructions running in one thread. The processor is doing an ok job filling these instructions, but a language and compiler can do a much better job. C doesn't collect any information about data dependencies, and instead just pretends all instructions are sequential. Even code which contains tight loops of sequential commands can be optimized, because you have an entire program and operating system running around that sequential code.