add-sgl-kernel

You add custom CUDA kernels to sgl-kernel by following a guided workflow for code creation, registration, and testing to integrate high-performance tensor operations as PyTorch extensions.

Registering a new C++ kernel in sgl-kernel involves extending the library with your custom operation, such as elementwise scaling, and explicitly registering it for GPU execution to enable high-performance tensor computations.

Yes, you can benchmark performance when integrating CUDA kernels into sgl-kernel, as the workflow specifically includes running benchmarks to validate and measure the efficiency of your custom GPU tensor operations.

You need custom CUDA kernel development when optimizing specific deep learning model computations, allowing you to implement operations like elementwise scaling for efficient GPU execution and deployment.

The best way to test custom tensor operations in sgl-kernel is to use the framework's integrated testing phase, which validates your newly registered CUDA/C++ kernels for correct and efficient execution before deployment.