What about OpenCL and CUDA C++ alternatives?

5 hours ago

OpenCL was created as a portable GPU programming model but failed to become dominant in AI due to slow committee-driven development and fragmentation.
Apple contributed OpenCL to Khronos but later abandoned it due to the inability to move fast and secretively, leading to the development of Metal.
Vendor-specific extensions and lack of a shared reference implementation caused OpenCL to become fragmented, weakening its portability.
OpenCL lacks standardized support for key AI hardware features like Tensor Cores, resulting in significant performance gaps compared to CUDA.
NVIDIA's CUDA succeeded by tightly co-designing with frameworks like TensorFlow and PyTorch, ensuring optimized performance on NVIDIA hardware.
The lessons from OpenCL's failure highlight that successful systems need fast iteration, unified strategy, open collaboration, and high-level abstractions.
Chris Lattner argues that committee efforts like OpenCL or vendor-controlled projects like OneAPI cannot succeed in unifying AI compute.
The post suggests that the future of AI compute requires addressing scalability beyond manual coding, pointing to upcoming discussions on AI compiler stacks.

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