4 ms·

Same here. What else can you do to ship GPU acceleration to AMD and NVIDIA people? The alternatives recommended here aren't even serious IMHO. I'd rather switc

by gcp 9y ago

Same here. What else can you do to ship GPU acceleration to AMD and NVIDIA people? The alternatives recommended here aren't even serious IMHO. I'd rather switch to CUDA and wait till Intel/AMD sort out a REAL compatibility layer than deal with those. Unless I'm mistaken, HIP still requires a separate compile for either platform and what runtime do they expect end users to have exactly?! At least CUDA and OpenCL are integrated in the vendor drivers. Vulkan compute with SPIR-V seems to be the only real solution, but even that is still very early. Sill waiting for proper OpenCL 2.0 support in NVIDIA drivers :P

freeone3000 9y ago
You simply don't ship. Enterprise deep learning doesn't ship their training code - large models are trained on purpose-designed, dedicated hardware. Hardware compatability doesn't matter, software does. Even that's flexible if it's significantly faster. (The models can be executed on low-powered, commodity CPUs. No need for any GPU there.)
- gcp 9y ago
  You simply don't ship That's totally an option for our product, great idea! Why did I never think of this! No seriously we are shipping, using OpenCL and it gives about a 20 times performance advantage for most users regardless if they have AMD or NVIDIA hardware. If something that's actually better than OpenCL comes along (or if AMD RTG goes out of business) I'll switch to it no heart broken. But that hasn't happened yet.
- joefourier 9y ago
  General-purpose programming on the GPU has many use cases apart from deep learning, e.g. image processing, computer vision, offline rendering and other high performance computing applications. OpenCL allows you to have a single code-base working on NVidia, AMD and Intel GPUs without having to recompile or put any special effort - the same kernel that works on NVidia will work anywhere.

by gcp 9y ago

freeone3000 9y ago

You simply don't ship. Enterprise deep learning doesn't ship their training code - large models are trained on purpose-designed, dedicated hardware. Hardware compatability doesn't matter, software does. Even that's flexible if it's significantly faster. (The models can be executed on low-powered, commodity CPUs. No need for any GPU there.)

gcp 9y ago
You simply don't ship That's totally an option for our product, great idea! Why did I never think of this! No seriously we are shipping, using OpenCL and it gives about a 20 times performance advantage for most users regardless if they have AMD or NVIDIA hardware. If something that's actually better than OpenCL comes along (or if AMD RTG goes out of business) I'll switch to it no heart broken. But that hasn't happened yet.
joefourier 9y ago
General-purpose programming on the GPU has many use cases apart from deep learning, e.g. image processing, computer vision, offline rendering and other high performance computing applications. OpenCL allows you to have a single code-base working on NVidia, AMD and Intel GPUs without having to recompile or put any special effort - the same kernel that works on NVidia will work anywhere.