sglang-diffusion-ako4all-kernel

To profile and tune diffusion kernels in SGLang, use the AKO4ALL workflow to clone repositories, run performance profiling, benchmark iterations, and validate results before porting improvements back into the SGLang codebase.

Benchmarking CUDA diffusion kernels for production requires iterating with profiling tools like ncu guided by AKO4ALL, maintaining clean repositories, and validating performance improvements against baseline metrics before integration.

Yes, AKO4ALL workflows support optimizing custom CUDA kernels for SGLang by facilitating setup, performance profiling, bottleneck identification, and structured iteration cycles for research and production development.

The AKO4ALL workflow handles diffusion kernel validation by enforcing discipline through cloning, verifying, and maintaining clean repositories, ensuring performance improvements are systematically validated before porting back to SGLang.

Maintaining a clean AKO4ALL repository is necessary to ensure seamless optimization cycles, preventing environment conflicts and ensuring that performance profiling and benchmarking results remain accurate and reproducible.