cpu-basics

To write a CPU kernel for PyTorch extensions, follow a standardized five-step C++ pattern covering tensor handling, type support, and memory-safe boundary checks to build portable operators for ML workflows.

Tensor contiguity in CPU kernels refers to how tensor data is laid out in memory. You must check and handle contiguity to ensure safe memory access and prevent data misalignment when executing C++ kernel operations across x86_64 and aarch64.

Supporting multiple data types like float32 and int64 in custom PyTorch CPU kernels requires implementing type handling within your C++ extension. This ensures your kernel can process diverse tensor inputs safely across different ML workflows.

Yes, you can use OpenMP for parallel processing in PyTorch C++ CPU kernels. There are specific usage rules to follow that ensure safe and fast execution when building portable, memory-safe kernel modules for production environments.

Best practices for memory safety in CPU kernel development include implementing safe boundary checks, managing tensor contiguity, and following a standardized five-step pattern to prevent memory access violations in C++ extensions.

Yes, this CPU kernel pattern works on both x86_64 and aarch64 architectures. It is designed to create portable, memory-safe C++ kernel modules that operate reliably across different hardware platforms in ML workflows.