Core Concepts

Nsight Python operates through three key primitives:

1. Annotations An annotation wraps a region of code that launches GPU kernels and tags them for attribution. Annotations can be used as decorators or context managers:

@nsight.annotate("torch")
def torch_kernel():
    ...

# or
with nsight.annotate("cutlass4"):
    cutlass_kernel()

By default, each annotation is expected to contain a single kernel launch. For more detailed information about handling multiple kernels within an annotation, see the API documentation.

2. Kernel Analysis Decorator Use nsight.analyze.kernel() to annotate a benchmark function. Nsight Python will rerun this function one configuration at a time. You can provide configurations in two ways:

• At decoration time using the configs parameter.

• At function call time by passing configs directly as an argument when invoking the decorated function.

@nsight.analyze.kernel
def benchmark(s):
    ...

benchmark(configs=[(1024,), (2048,)])

3. Plot Decorator Add nsight.analyze.plot() to automatically generate plots from your profiling runs.

@nsight.analyze.plot(filename="plot.png", ylabel="Runtime (ns)")
@nsight.analyze.kernel(configs=[(1024,), (2048,)])
def benchmark(s):
    ...