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You’re right. The hard part is to manage processing of your data as close to the metal as possible to optimize memory, data flow, and computation. Coming from
by qrios 3y ago
You’re right. The hard part is to manage processing of your data as close to the metal as possible to optimize memory, data flow, and computation.
Coming from FPGA (both Verilog and VHDL), to me CUDA offers the best way to handle all three parts in comparison to let‘s say OpenCL, Vulkan, or Metal.
For game developers this might be different, as here you always use high-level language and packages (in most cases).
AMD need‘s to rethink there strategy regarding compute tool stack radically. If the HelloWorldMatrixMul is longer as ten lines and not compile on every new AMD powered notebook out of the box, they have no chance to beat NVIDIA. (I know, that the last point is not given for NVIDIA. But it would give AMD the doubleplus good, currently missing.)
- Arelius 3y ago> For game developers this might be different, as here you always use high-level language and packages (in most cases). ... I'm not sure what you mean that you always use high-level language and packages... But it doesn't sound right. The reason game developers use DX/VK/GL as opposed to cuda is entirely: A. You want access to non-nvidia customers B. You need access to more aspects of the hardware then is exposed to Cuda. Cuda provides a nice interface to Nvidia's compute. Almost no access to the entire rest of the fixed function pipeline.
- qrios 3y agoI was referencing DX/VK/GL and C++. What I can see on the lowest compute level, ML libs often are using pure CUDA in C or even PTX and are used especially if the code is generated by let's say via Python.