kernelfoundry.eval_pipeline.profiler_command

Profiler command wrappers for different profilers (Unitrace, NCU, VTune)

Functions

get_profilers(language, arch[, profiler_type])

Get the appropriate profiler class based on user-specified profiler type or language & GPU architecture.

Classes

NCU(output_dir[, ncu_cmd])	Nsight Compute profiler helper
OCLUnitrace(output_dir[, group, timeline, ...])	Helper to run Unitrace for OpenCL kernels with metric-sampling
Profiler(output_dir)	Abstract base class for profiler helpers
Unitrace(output_dir[, group, timeline, ...])	Unitrace profiler helper
VTune(output_dir[, vtune_cmd])	VTune profiler helper for Intel GPUs

class kernelfoundry.eval_pipeline.profiler_command.Profiler(output_dir: Path | str)

Abstract base class for profiler helpers

__init__(output_dir: Path | str)

abstract property name: str

Return the name of the profiler

abstract wrap_cmd(cmd: str) → str

Wrap the given command with profiler-specific command :param cmd: The original command to run the program without profiling

Returns:

A wrapped command that includes the original command and any additional commands needed for profiling.

env_vars()

Return a dictionary of environment variables to update or add when running the command

end_marker() → str | None

Return the end-of-process marker string for this profiler.

The marker is passed to robust_subprocess_run to detect when the profiled process has finished and can safely be terminated.

The default "pytest" activates the pytest-summary monitor, which waits for the pytest result line. Return a different string to watch for that literal in the output, or None to disable early termination entirely.

prepare(host_output_dir: Path) → None

Prepare the host output directory before running the profiler command. Override in subclasses that need to stage files into the host directory (e.g. scripts that must be accessible inside a container).

abstract read_output() → dict[str, str]

Read the profiler output into a dictionary that maps the filename to its content

class kernelfoundry.eval_pipeline.profiler_command.Unitrace(output_dir: Path | str, group: str = 'ComputeBasic', timeline: bool = True, unitrace_cmd: str = 'unitrace')

Unitrace profiler helper

__init__(output_dir: Path | str, group: str = 'ComputeBasic', timeline: bool = True, unitrace_cmd: str = 'unitrace')

property name: str

Return the name of the profiler

wrap_cmd(cmd: str) → str

Wrap the given command with unitrace profiling command

env_vars()

Return a dictionary of environment variables to update or add when running the command

read_output() → dict[str, str]

Read the profiler output into a dictionary that maps the filename to its content

class kernelfoundry.eval_pipeline.profiler_command.OCLUnitrace(output_dir: Path | str, group: str = 'ComputeBasic', timeline: bool = True, unitrace_cmd: str = 'unitrace')

Helper to run Unitrace for OpenCL kernels with metric-sampling

end_marker() → str | None

Return the end-of-process marker string for this profiler.

The marker is passed to robust_subprocess_run to detect when the profiled process has finished and can safely be terminated.

The default "pytest" activates the pytest-summary monitor, which waits for the pytest result line. Return a different string to watch for that literal in the output, or None to disable early termination entirely.

prepare(host_output_dir: Path) → None

Copy the retry script into the host output directory so it is accessible inside the container (where host_output_dir is mounted as self.output_dir).

wrap_cmd(cmd: str) → str

Wrap the given command with unitrace profiling command for OpenCL using retry script

class kernelfoundry.eval_pipeline.profiler_command.NCU(output_dir: Path | str, ncu_cmd: str = 'ncu')

Nsight Compute profiler helper

__init__(output_dir: Path | str, ncu_cmd: str = 'ncu')

property name: str

Return the name of the profiler

wrap_cmd(cmd: str) → str

Wrap the given command with NCU profiling command

read_output() → dict[str, str]

Read the profiler output into a dictionary that maps the filename to its content

class kernelfoundry.eval_pipeline.profiler_command.VTune(output_dir: Path | str, vtune_cmd: str = 'vtune')

VTune profiler helper for Intel GPUs

__init__(output_dir: Path | str, vtune_cmd: str = 'vtune')

property name: str

Return the name of the profiler

wrap_cmd(cmd: str) → str

Wrap the given command with VTune collection and hotspot report generation.

Chains two commands with && so all vtune invocations happen inside the same environment (e.g. container) where vtune is available:

1. vtune -collect (paused; the kernel under test resumes it via ITT)

2. vtune -report hotspots -> tsv file

read_output() then simply reads that file without invoking vtune.

end_marker() → str | None

Return the end-of-process marker string for this profiler.

The marker is passed to robust_subprocess_run to detect when the profiled process has finished and can safely be terminated.

The default "pytest" activates the pytest-summary monitor, which waits for the pytest result line. Return a different string to watch for that literal in the output, or None to disable early termination entirely.

env_vars()

Return a dictionary of environment variables to update or add when running the command

read_output() → dict[str, str]

Read the pre-generated TSV report and return a counters JSON.

The TSV file was written by the vtune -report command chained in wrap_cmd, so no vtune invocation is needed here.

kernelfoundry.eval_pipeline.profiler_command.get_profilers(language: str, arch: str, profiler_type: str | None = None) → list[type[Profiler]]

Get the appropriate profiler class based on user-specified profiler type or language & GPU architecture.