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Why can't a GPU power a computer?

by zsrxx 7y ago

Retric 7y ago
The biggest issue is lack of huge caches, and the extreme latency to main memory due to the speed of light. GPU’s are fine or even better for a wide range of compute tasks see CUDA etc, they just end up being very slow at a subset of common tasks.
- hermitdev 7y ago
  Circuits don't operate at speed of light, though. People like to say this over and over again, but electron mobility through a circuit is only around 2/3s the speed of light, IIRC. They may have very tiny mass, but they still have mass.
  - mycall 7y ago
    It is the mass in the electrons that creates the heat.
  - jabl 7y ago
    No, it's the electromagnetic field which propagates at around 2/3c in the conductor. The electron drift velocity is on the order of a fraction of a mm/s.
ArnoVW 7y ago
Same reason we have OLAP and OLTP systems. Same reason it makes sense for some use cases to set up a Hadoop cluster, and for others to have a single but fast CPU: some tasks can be run in parallel, others can't. See Amdahl's law : https://en.wikipedia.org/wiki/Amdahl%27s_law https://en.wikipedia.org/wiki/Amdahl%27s_law
joking 7y ago
You can calculate the value of each screen pixel without knowing the value of the others. To calculate the n value of a fibonnaci sequence you have to calculate the n-1 and n-2 values first, so it doesn't matter how many cores do you have. Gpus are for the former and CPUs for the latest.
- chillee 7y ago
  Nitpick: I understand your point in general, but it's not actually true that you need to calculate the n-1 and n-2 values first for Fibonacci. You can use Binet's formula or matrix exponentiation to calculate it without such a dependency.
tenebrisalietum 7y ago
I'm not an expert and might be horribly wrong and welcome edification, but I believe you don't want your GPU powering your computer because - GPUs don't handle I/O other than writing to video RAM. - GPUs don't handle interrupts. - GPUs don't handle branching well. For the first two, a lot of infrastructure that is part of the chipset and platform would have to be extended to each compute unit of the GPU. Imagine a database on a GPU. It's not like 1.5K-2k+ cores that are super good at math can read and write your disk or disk array at once.
Faark 7y ago
GPUs are made to execute a limited set of the same operations operation on on a huge amount of data in parallel. This is a totally different workload than your usual computer programs, that commonly have a long and complicated series of commands. Thus just translating your program 1:1 to your GPU computer would make it way slower. Your GPU runs probably around ~1.5GHz, a third of your CPUs. The GPU does have a few thousand cores that run in parallel while our consumer CPUs does have at best a dozen (that are more capable than any single GPU core), but most of those are often already idle, since it's a lot of work to make your software take advantage of them. For some tasks it's worth it, and those take advantage of e.g. you GPU.

Why can't a GPU power a computer?

by zsrxx 7y ago

Why can't a GPU power a computer?

The biggest issue is lack of huge caches, and the extreme latency to main memory due to the speed of light. GPU’s are fine or even better for a wide range of compute tasks see CUDA etc, they just end up being very slow at a subset of common tasks.

hermitdev 7y ago
Circuits don't operate at speed of light, though. People like to say this over and over again, but electron mobility through a circuit is only around 2/3s the speed of light, IIRC. They may have very tiny mass, but they still have mass.
- mycall 7y ago
  It is the mass in the electrons that creates the heat.
- jabl 7y ago
  No, it's the electromagnetic field which propagates at around 2/3c in the conductor. The electron drift velocity is on the order of a fraction of a mm/s.

ArnoVW 7y ago

Same reason we have OLAP and OLTP systems. Same reason it makes sense for some use cases to set up a Hadoop cluster, and for others to have a single but fast CPU: some tasks can be run in parallel, others can't. See Amdahl's law : https://en.wikipedia.org/wiki/Amdahl%27s_law https://en.wikipedia.org/wiki/Amdahl%27s_law

joking 7y ago

You can calculate the value of each screen pixel without knowing the value of the others. To calculate the n value of a fibonnaci sequence you have to calculate the n-1 and n-2 values first, so it doesn't matter how many cores do you have. Gpus are for the former and CPUs for the latest.

chillee 7y ago
Nitpick: I understand your point in general, but it's not actually true that you need to calculate the n-1 and n-2 values first for Fibonacci. You can use Binet's formula or matrix exponentiation to calculate it without such a dependency.

tenebrisalietum 7y ago

I'm not an expert and might be horribly wrong and welcome edification, but I believe you don't want your GPU powering your computer because - GPUs don't handle I/O other than writing to video RAM. - GPUs don't handle interrupts. - GPUs don't handle branching well. For the first two, a lot of infrastructure that is part of the chipset and platform would have to be extended to each compute unit of the GPU. Imagine a database on a GPU. It's not like 1.5K-2k+ cores that are super good at math can read and write your disk or disk array at once.

Faark 7y ago

GPUs are made to execute a limited set of the same operations operation on on a huge amount of data in parallel. This is a totally different workload than your usual computer programs, that commonly have a long and complicated series of commands. Thus just translating your program 1:1 to your GPU computer would make it way slower. Your GPU runs probably around ~1.5GHz, a third of your CPUs. The GPU does have a few thousand cores that run in parallel while our consumer CPUs does have at best a dozen (that are more capable than any single GPU core), but most of those are often already idle, since it's a lot of work to make your software take advantage of them. For some tasks it's worth it, and those take advantage of e.g. you GPU.