fla-nvidia-performance

You can profile NVIDIA GPU kernel performance in the Flash Linear Attention (FLA) repository using Nsight Compute, which provides detailed metrics required for analyzing and optimizing kernel throughput.

The best way to benchmark training throughput after kernel optimization is to run the benchmark_training_throughput.py script with your specific workload to evaluate performance improvements and collect evidence.

Yes, GPU performance testing requires establishing minimum and preferred hardware baseline configurations to ensure your kernel optimizations are accurately evaluated against consistent hardware standards.

Yes, Nsight Compute is used for detailed profiling to generate the performance evidence required to validate that your kernel optimizations meet the established throughput benchmarks.

Your kernel optimization lacks performance evidence because it was not profiled using Nsight Compute against established hardware baselines to verify training and generation throughput improvements.

To benchmark GPU kernel performance in FLA, you need Nsight Compute (ncu) for profiling and Python to execute the benchmarking scripts that measure training and generation throughput.