3 ms·

Curious how this stacks up against CUDA(from a programming perspective, almost guarantee CUDA is faster). Does this provide a way to manage/copy memory back an

Curious how this stacks up against CUDA(from a programming perspective, almost guarantee CUDA is faster). Does this provide a way to manage/copy memory back and forth from the GPU? Or is it just allowing you to compile some code for the GCN and the rest is up to you?

twtw 8y ago
This appears to be a GCN backend intended for use with OpenMP and friends, so not really comparable to cuda (more comparable maybe to the llvm nvptx backend).
baybal2 8y ago
Why CUDA is faster than OpenCL?
- roel_v 8y ago
  More work put into implementation, better (more optimized) libraries, some additional functionality that lets you do things faster in some circumstances. Cuda doesn't have to care about anything but nvidia gpus, opencl needs to run on anything.
- shmerl 8y ago
  It's not faster from what I've heard. It just has more libraries around it.
- sharpneli 8y ago
  It used to be on par on nv hardware. Then nvidia just stopped improving their OpenCL backend. Otherwise assuming no fancy features are used they are identical in their programming model.
  - TomVDB 8y ago
    AFAIK the part of identical programming models isn’t the case anymore since Volta, because Volta and Turing have now progression guarantees when you have intra-warp divergence due to the presence of unique program counters for each thread. Before that, intro-warp divergence combined with badly placed synchronization operations could result in hard hangs. I don't think this makes a different in terms of raw low level performance, but it might have an impact in terms of implementation algorithms that require synchronization?
dragontamer 8y ago
OpenMP 4.5 provides the #pragma target data, target enter data, target exit data, and target update constructs to provide for the memory-management to-and-from the GPU. omp_target_alloc, omp_target_memcpy, omp_target-associate_ptr provide some other utility functions that you'd expect for these use cases. omp_target_memcpy can copy from host-to-device, device-to-host, or even device-to-device.

twtw 8y ago

This appears to be a GCN backend intended for use with OpenMP and friends, so not really comparable to cuda (more comparable maybe to the llvm nvptx backend).

baybal2 8y ago

Why CUDA is faster than OpenCL?

roel_v 8y ago
More work put into implementation, better (more optimized) libraries, some additional functionality that lets you do things faster in some circumstances. Cuda doesn't have to care about anything but nvidia gpus, opencl needs to run on anything.
shmerl 8y ago
It's not faster from what I've heard. It just has more libraries around it.
sharpneli 8y ago
It used to be on par on nv hardware. Then nvidia just stopped improving their OpenCL backend. Otherwise assuming no fancy features are used they are identical in their programming model.
- TomVDB 8y ago
  AFAIK the part of identical programming models isn’t the case anymore since Volta, because Volta and Turing have now progression guarantees when you have intra-warp divergence due to the presence of unique program counters for each thread. Before that, intro-warp divergence combined with badly placed synchronization operations could result in hard hangs. I don't think this makes a different in terms of raw low level performance, but it might have an impact in terms of implementation algorithms that require synchronization?

dragontamer 8y ago

OpenMP 4.5 provides the #pragma target data, target enter data, target exit data, and target update constructs to provide for the memory-management to-and-from the GPU. omp_target_alloc, omp_target_memcpy, omp_target-associate_ptr provide some other utility functions that you'd expect for these use cases. omp_target_memcpy can copy from host-to-device, device-to-host, or even device-to-device.