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To be honest, although GPUs are super cool, I have to admit a bit of disappointment in practice. I don't know about databases, but in the machine learning comm

To be honest, although GPUs are super cool, I have to admit a bit of disappointment in practice. I don't know about databases, but in the machine learning community ones sees usually about 6x to 20x speedup on GPU. This is amazing, of course, but far below what, way back, naively thought we'd be able to get from GPUs -- i.e., 1000 processors instead of 1, we should get a 1000x speed-up, right? Not so at all. So many architectural issues (i/o, memory, even sometimes algorithmic limitations) get in the way of achieve true parallel linear performance.

jacquesm 10y ago
GPUs typically clock at roughly half of what a regular desktop CPU will do so there's a factor of two right there. Furthermore the data has to pass through another bottleneck (twice!), the PCIe bus. Finally, you need to keep in mind that all this is subject to Amdahl's law, you still have a sequential component that you won't be able to speed up at all. Also, that 6 to 20x speedup is what you get on the commodity GPUs, the ones sold at a higher mark-up are quite a bit faster than that due to better comms options and more RAM.
- deepnotderp 10y ago
  Eh, Amdahl's law usually isn't all too applicable to highly parallelizable tasks, check out Gustafson's Law for a more modern perspective on the parallelization paradox, courtesy of supercomputers being forced to parallelize.
deepnotderp 10y ago
Each CUDA core can tackle approximately one FLOP per cycle, while a CPU core can tackle approximately 16 FLOPs per cycle. Given that there are usually at least 6 cores on a chip, and that reaching full utilization is difficult on a GPU, I dont' think ~20X is too shabby. Also more importantly, the efficiency goes up dramatically, which is usually infinitely more important than the raw performance.
- Sunset 10y ago
  You can also throw extra GPUs at the PCI bus, not so with extra processors.

jacquesm 10y ago

GPUs typically clock at roughly half of what a regular desktop CPU will do so there's a factor of two right there. Furthermore the data has to pass through another bottleneck (twice!), the PCIe bus. Finally, you need to keep in mind that all this is subject to Amdahl's law, you still have a sequential component that you won't be able to speed up at all. Also, that 6 to 20x speedup is what you get on the commodity GPUs, the ones sold at a higher mark-up are quite a bit faster than that due to better comms options and more RAM.

deepnotderp 10y ago
Eh, Amdahl's law usually isn't all too applicable to highly parallelizable tasks, check out Gustafson's Law for a more modern perspective on the parallelization paradox, courtesy of supercomputers being forced to parallelize.

deepnotderp 10y ago

Each CUDA core can tackle approximately one FLOP per cycle, while a CPU core can tackle approximately 16 FLOPs per cycle. Given that there are usually at least 6 cores on a chip, and that reaching full utilization is difficult on a GPU, I dont' think ~20X is too shabby. Also more importantly, the efficiency goes up dramatically, which is usually infinitely more important than the raw performance.

Sunset 10y ago
You can also throw extra GPUs at the PCI bus, not so with extra processors.