3 ms·

> "now each machine needs a couple of pricey GPUs." Well it would be much more pricey to get the same amount of flops out of traditional CPUs.

by sampo 13y ago

seanmcdirmid 13y ago
Right, but GPUs are something that the BigData crowd don't really know how to leverage yet.
- pjmlp 13y ago
  I think the situation might improve as we get more languages to run in the GPUs. The current approach of both CUDA and OpenCL camps to move to some sort of standard intermediate representation is a step in the right direction. This might open the door to more declarative approaches for data modelling in the GPU.
  - seanmcdirmid 13y ago
    Right now you can't get any closer to the metal then you can with CUDA [1]; there is very little memory abstractions and programming is imperative in the worst possible way; you basically are scheduling your own memory bandwidth! In the short term, I don't see a way around this. Functional programming won't be effective unless some kind of advanced synthesis takes hold (you can write GPU code with Haskell, but the ceiling is low). [1] Or OpenCL, though I don't know anyone that uses it...too slow.
    - pjmlp 13y ago
      Yeah, CUDA is way better designed than OpenCL, specially given its support for C++ and FORTRAN at compiler level. Up to recently OpenCL was all about C. Plus it followed the same broken model as GLSL shaders, requiring low level compiler as library. I think OpenCL camp is mostly unaware how advanced CUDA is in regard to language support, since they live in pure C land. Better performance is a consequence of NVidia cards being quite good in regards to the competition and they pushing their own tech. Overall you are quite right. As I mentioned, I see the move to PTX, HSAIL and SPIR as a small step in that direction. More needs to happen in terms of the whole architecture.
      - sampo 13y ago
        > "FORTRAN" Old habits die hard, but for the past 20 years, the official spelling has been: Fortran.
mturk 13y ago
It depends on if you are counting hardware costs or software costs as well. The rough estimate I heard recently is that an hour of programmer time costs about the same as an hour of a 100 Teraflop computer.

> "now each machine needs a couple of pricey GPUs." Well it would be much more pricey to get the same amount of flops out of traditional CPUs.

by sampo 13y ago

> "now each machine needs a couple of pricey GPUs." Well it would be much more pricey to get the same amount of flops out of traditional CPUs.

seanmcdirmid 13y ago

Right, but GPUs are something that the BigData crowd don't really know how to leverage yet.

pjmlp 13y ago
I think the situation might improve as we get more languages to run in the GPUs. The current approach of both CUDA and OpenCL camps to move to some sort of standard intermediate representation is a step in the right direction. This might open the door to more declarative approaches for data modelling in the GPU.
- seanmcdirmid 13y ago
  Right now you can't get any closer to the metal then you can with CUDA [1]; there is very little memory abstractions and programming is imperative in the worst possible way; you basically are scheduling your own memory bandwidth! In the short term, I don't see a way around this. Functional programming won't be effective unless some kind of advanced synthesis takes hold (you can write GPU code with Haskell, but the ceiling is low). [1] Or OpenCL, though I don't know anyone that uses it...too slow.
  - pjmlp 13y ago
    Yeah, CUDA is way better designed than OpenCL, specially given its support for C++ and FORTRAN at compiler level. Up to recently OpenCL was all about C. Plus it followed the same broken model as GLSL shaders, requiring low level compiler as library. I think OpenCL camp is mostly unaware how advanced CUDA is in regard to language support, since they live in pure C land. Better performance is a consequence of NVidia cards being quite good in regards to the competition and they pushing their own tech. Overall you are quite right. As I mentioned, I see the move to PTX, HSAIL and SPIR as a small step in that direction. More needs to happen in terms of the whole architecture.
    - sampo 13y ago
      > "FORTRAN" Old habits die hard, but for the past 20 years, the official spelling has been: Fortran.

mturk 13y ago

It depends on if you are counting hardware costs or software costs as well. The rough estimate I heard recently is that an hour of programmer time costs about the same as an hour of a 100 Teraflop computer.