Workflow

Workflow#

The following diagram briefly demonstrates how KernelGen helps you to generate Kernel.

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The generation process is as follows:

  1. Collect Kernel information: User enters semantic operator definitions into KernelGen, for example, by referring to the ReLU operator definitions. KernelGen collects operator basic parameters from the definitions.

  2. Search code snippets: KernelGen searches code snippets similar to user’s definitions as reference and extracts Kernel parameters. During this step, user can select to use the searched code snippets or not.

  3. Generate Kernel code and CUDA implementation code: KernelGen generates codes of Kernel and CUDA implementation. CUDA implementation is used as a PyTorch benchmark reference.

  4. Test Kernel based on CUDA implementation code：KernelGen tests Kernel based on the PyTorch benchmark and outputs the test results of Correctness and Speedup Ratio.