4. Nsight Compute CLI

The User Guide for Nsight Compute CLI.

4.1. Introduction

NVIDIA Nsight Compute CLI (ncu) provides a non-interactive way to profile applications from the command line. It can print the results directly on the command line or store them in a report file. It can also be used to simply launch the target application (see General for details) and later attach with NVIDIA Nsight Compute or another ncu instance.

For users migrating from nvprof to NVIDIA Nsight Compute, please additionally see the Nvprof Transition Guide for comparison of features and workflows.

4.2. Quickstart

1. Launch the target application with the command line profiler

   The command line profiler launches the target application, instruments the target API, and collects profile results for the specified kernels. The CLI executable is called ncu. A shortcut with this name is located in the base directory of the NVIDIA Nsight Compute installation. The actual executable is located in the folder target\windows-desktop-win7-x64 on Windows or target/linux-desktop-glibc_2_11_3-x64 on Linux. By default, NVIDIA Nsight Compute is installed in /usr/local/cuda-<cuda-version>/NsightCompute-<version> on Linux and in C:\Program Files\NVIDIA Corporation\Nsight Compute <version> on Windows.

   To collect the basic set for all kernel launches in the target application, launch:

   $ ncu -o profile CuVectorAddMulti.exe
   

   The application runs in instrumented mode and for each kernel launch, a profile result is created. The results are written by default to profile.nsight-cuprof. Each output from the compute profiler starts with ==PROF== The other lines are output from the application itself. For each profiled kernel, the name of the kernel function and the progress of data collection is shown. To collect all requested profile information, it may be required to replay the kernels multiple times. The total number of replay passes per kernel is shown after profiling has completed.

   [Vector addition of 1144477 elements]
   ==PROF== Connected to process 5268
   Copy input data from the host memory to the CUDA device
   CUDA kernel launch A with 4471 blocks of 256 threads
   ==PROF== Profiling "vectorAdd_A" - 0: 0%....50%....100% - 46 passes
   CUDA kernel launch B with 4471 blocks of 256 threads
   ==PROF== Profiling "vectorAdd_B" - 1: 0%....50%....100% - 46 passes
   Copy output data from the CUDA device to the host memory
   Done
   ==PROF== Disconnected from process 5268
   ==PROF== Report: profile.ncu-rep
   

2. Customizing data collection

   Options are available to specify for which kernels data should be collected. -c limits the number of kernel launches collected. -s skips the given number of kernels before data collection starts. -k allows you to filter the kernels by a regex match of their names. --kernel-id allows you to filter kernels by context, stream, name and invocation, similar to nvprof.

   To limit what should be collected for each kernel launch, specify the exact *.section (files) by their identifier using --section. Each section file defines a set of metrics to be collected, grouped logically to solve a specific performance question. By default, the sections associated with the basic set are collected. Use --list-sets to see the list of currently available sets. Use --list-sections to see the list of currently available sections. The default search directory and location of pre-defined section files is also called sections/. See the Profiling Guide for more details.

   Alternatively, you can collect a set of individual metrics using --metrics. The available metrics can be queried using --query-metrics. For an explanation of the naming conventions and structuring of metrics, see Metrics Structure.

   Most metrics in NVIDIA Nsight Compute are named using a base name and various suffixes, e.g. sm__throughput.avg.pct_of_peak_sustained_elapsed. The base name is sm__throughput and the suffix is avg.pct_of_peak_sustained_elapsed. This is because most metrics follow the same structure and have the same set of suffixes. You need to pass the base or full name to NVIDIA Nsight Compute when selecting a metric for profiling. Use --query-metrics-mode suffix --metrics <metrics list> to see the full names for the chosen metrics.

   Some additional metrics do not follow this structured naming. They are documented in the Metrics Reference.

3. Changing command line output

   By default, a temporary file is used to store profiling results, and data is printed to the command line. To permanently store the profiler report, use -o to specify the output filename.

   Besides storing results in a report file, the command line profiler can print results using different pages. Those pages correspond to the respective pages in the UI’s report. By default, the Details page is printed, if no explicit output file is specified. To select a different page or print in addition to storing in an explicit file, use the --page=<Page> command. Currently, the following pages are supported: details, raw, source.

   Use --csv to make any output comma separated and easier to process further. See Console Output for further options, e.g. summary views.

4. Open the report in the UI

   The UI executable is called ncu-ui. A shortcut with this name is located in the base directory of the NVIDIA Nsight Compute installation. The actual executable is located in the folder host\windows-desktop-win7-x64 on Windows or host/linux-desktop-glibc_2_11_3-x64 on Linux. In the UI window, close the Start Activity dialog and open the report file through File > Open, by dragging the report file into NVIDIA Nsight Compute.

   You can also specify the report file as a command line parameter to the executable, i.e. as ncu-ui <MyReport.ncu-rep>. Alternatively, when using NVIDIA Nsight Compute CLI on a platform with host support, --open-in-ui can be used directly with ncu to open a collected report in the user interface.

   The report opens in a new document window. For more information about the report, see the Profiler Report for collecting profile information through NVIDIA Nsight Compute.

4.3. Usage

4.3.1. Modes

Modes change the fundamental behavior of the command line profiler. Depending on which mode is chosen, different Command Line Options become available. For example, Launch is invalid if the Attach mode is selected.

• Launch-and-attach: The target application is launched on the local system with the tool’s injection libraries. Depending on which profiling options are chosen, selected kernels in the application are profiled and the results printed to the console or stored in a report file. The tool exits once the target application finishes or crashes, and once all results are processed.

  This is the default, and the only mode that supports profiling of child processes on selected platforms.

• Launch: The target application is launched on the local system with the tool’s injection libraries. As soon as the first intercepted API call is reached (commonly cuInit()), all application threads are suspended. The application now expects a tool to attach for profiling. You can attach using NVIDIA Nsight Compute or using the command line profiler’s Attach mode.

• Attach: The tool tries to connect to a target application previously launched using NVIDIA Nsight Compute or using the command line profiler’s Launch mode. The tool can attach to a target on the local system or using a remote connection.

4.3.2. Multi-Process Support

NVIDIA Nsight Compute CLI supports profiling multi-process applications on the following platforms: x86_64 Windows, x86_64 Linux, DRIVE OS Linux, DRIVE OS QNX, PowerPC. See the Launch options on how to enable this feature.

On x86_64 Windows, NVIDIA Nsight Compute CLI supports profiling 64-bit processes launched from 32-bit applications by default . On x86_64 Linux, launching from 32-bit applications requires you to enable the support-32bit option, and the required 32-bit libraries must be installed on your system. On DRIVE OS Linux, DRIVE OS QNX and PowerPC, tracking of 32-bit applications is not supported. Profiling of 32-bit processes is not supported on any platform.

Profiling MPI applications is a special case of multi-process profiling.

NVIDIA Nsight Compute CLI can be used to profile applications launched with the mpirun command.

• To profile all ranks on a node and store all the profiling data in a single report file:

  ncu --target-processes all -o <report-name> mpirun [mpi arguments] <app> [app arguments]
  

• To profile multi-node submissions, one instance of NVIDIA Nsight Compute CLI can be used per node. Ensure that you specify unique report files per rank.

  mpirun [mpi arguments] ncu -o report_%q{OMPI_COMM_WORLD_RANK} <app> [app arguments]
  

• To profile a single rank one can use a wrapper script. The following script (called “wrap.sh”) profiles rank 0 only:

  #!/bin/bash
  if [[ $OMPI_COMM_WORLD_RANK == 0 ]]; then
     ncu -o report_${OMPI_COMM_WORLD_RANK}  --target-processes all "$@"
  else
     "$@"
  fi
  

  and then execute:

  mpirun [mpi arguments] ./wrap.sh <app> [app arguments]
  

4.3.3. Output Pages

The command line profiler supports printing results to the console using various pages. Each page has an equivalent in NVIDIA Nsight Compute’s Profiler Report. In the command line profiler, they are slightly adapted to fit console output. To select a page, use the --page option. By default, the details page is used. Note that if --page is not used but --export is, no results will be printed to the console.

• Details: This page represents NVIDIA Nsight Compute’s Details page. For every profiled kernel launch, each collected is printed as section as a three-column table, followed by any rule results applied to this section. Rule results not associated with any section are printed after the kernel’s sections.

  The first section table column shows the metric name. If the metric was given a label in the section, it is used instead. The second column shows the metric unit, if available. The third column shows the unit value. Both metric unit and value are automatically adjusted to the most fitting order of magnitude. By default, only metrics defined in section headers are shown. This can be changed by passing the --details-all option on the command line.

  Some metrics will show multiple values, separated by “;”, e.g. memory_l2_transactions_global Kbytes 240; 240; 240; 240; 240. Those are instanced metrics, which have one value per represented instance. An instance can be a streaming multiprocessor, an assembly source line, etc.

• Raw: This page represents NVIDIA Nsight Compute’s Raw page. For every profiled kernel launch, each collected metric is printed as a three-column table. Besides metrics from sections, this includes automatically collected metrics such as device attributes and kernel launch information.

  The first column shows the metric name. The second and third columns show the metric unit and value, respectively. Both metric unit and value are automatically adjusted to the most fitting order of magnitude. No unresolved regex:, group:, or breakdown: metrics are included.

4.3.4. Profile Import

Using the --import option, saved reports can be imported into the command line profiler. When using this flag, most other options are not available, except for certain result filterting options. They are marked as such in the Profile options table.

4.3.5. Filtered Profile Export

Using the --import and --export options together, along with supported filtering options, you can export desired results from one report to another. Most of the filtering Profile options that can be used with --import alone are also supported here, except for --metrics and --section.

4.3.6. Metrics and Units

When available and applicable, metrics are shown along with their unit. This is to make it apparent if a metric represents cycles, threads, bytes/s, and so on.

By default, units are scaled automatically so that metric values are shown with a reasonable order of magnitude. Units are scaled using their SI-factors, i.e. byte-based units are scaled using a factor of 1000 and the prefixes K, M, G, etc. Time-based units are also scaled using a factor of 1000, with the prefixes n, u and m. This scaling can be changed using a command line option, see Console Output options for details.

4.3.7. NVTX Filtering

--nvtx-include <configuration> --nvtx-exclude <configuration>

These options are used to profile only those kernels which satisfy the conditions mentioned in the configuration. Through these options, you can choose which kernel falls into a specific range or collection of ranges.

You can use both options multiple times, mentioning all the --nvtx-include configurations followed by all --nvtx-exclude configurations. NVTX filtering requires --nvtx option.

NVTX ranges are of two types: NvtxRangeStart/End and NvtxRangePush/Pop. The configuration syntax for both the types are briefly described below. Both range and domain names can contain whitespace. Note that “Domain” and “range” in below example are for illustration purposes only and are not required to mark domain or range names.

• Push-Pop Ranges

  Quantifier	Description	Example
  /	Delimiter between range names. When only a single range name is given, the delimiter must be appended to indicate that this refers to a push/pop range.	A_range/ A_range/B range A_range/\*/B range
  [	Range is at the bottom of the stack	[A_range [A_range/ /Range Z
  ]	Range is at the top of the stack	Range Z] Range C/\*/Range Z]
  	Only one B rangeetween the two other ranges	B range/ /Range D
  *	Zero or more range(s) between the two other ranges	B range/\*/Range Z
  @	Specify domain name. If not mentioned, assuming <default domain>	Domain-A@A_range Domain B@A_range/\*/Range Z]

  Include kernels wrapped inside push/pop range ‘A_range’ of ‘<default-domain>’:

  ncu --nvtx --nvtx-include "A_range/" CuNvtx.exe
  

  Include kernels wrapped inside push/pop range ‘A_range’ of ‘Domain-A’:

  ncu --nvtx --nvtx-include "Domain-A@A_range/" CuNvtx.exe
  

  Include kernels wrapped inside push/pop range ‘A_range’ of ‘<default domain>’, where ‘A_range’ is at the bottom of the stack:

  ncu --nvtx --nvtx-include "[A_range" CuNvtx.exe
  

  Include kernels wrapped inside push/pop ranges ‘A_range’ and ‘B range’ of ‘<default domain>’, with zero or many ranges between them:

  ncu --nvtx --nvtx-include "A_range/*/B range" CuNvtx.exe
  

  Exclude kernels wrapped inside push/pop ranges ‘A_range’ and ‘B range’ of ‘<default domain>’, with zero or many ranges between them:

  ncu --nvtx --nvtx-exclude "A_range/*/B range" CuNvtx.exe
  

  Include kernels wrapped inside only push/pop range ‘A_range’ of ‘<default domain>’ but not inside ‘B range’ at the top of the stack:

  ncu --nvtx --nvtx-include "A_range/" --nvtx-exclude "B range]" CuNvtx.exe
  

• Start-End Ranges

  Quantifier	Description	Example
  ,	Delimiter between range names	A_range,B range B range,A_range,Range C
  @	Specify domain name. If not mentioned, assuming <default domain>	Domain-A@A_range Domain B@B range,Range Z

  Include kernels wrapped inside start/end range ‘A_range’ of ‘Domain-A’:

  ncu --nvtx --nvtx-include "Domain-A@A_range" CuNvtx.exe
  

  Include kernels wrapped inside both start/end ranges, ‘A_range’ and ‘B range’ of ‘<default domain>’:

  ncu --nvtx --nvtx-include "A_range,B range" CuNvtx.exe
  

  Include kernels wrapped inside start/end ranges, ‘A_range’ or ‘B range’ of ‘<default domain>’:

  ncu --nvtx --nvtx-include "A_range" --nvtx-include "B range" CuNvtx.exe
  

  Include all kernels, except those which are wrapped inside start/end range ‘A_range’ of ‘<default domain>’:

  ncu --nvtx --nvtx-exclude "A_range" CuNvtx.exe
  

  Include kernels wrapped inside only start/end ‘B range’ and not ‘A_range’ of ‘<default domain>’:

  ncu --nvtx --nvtx-include "B range"--nvtx-exclude "A_range" CuNvtx.exe
  

• Regular Expression Support

  The configuration syntax for both the types NvtxRangeStart/End and NvtxRangePush/Pop is the same. Additionally, to use regular expressions, follow the following syntax.

  • Provide prefix ‘regex:’ to treat nvtx config as regular expression.

    ncu --nvtx --nvtx-include "regex:Domain[A-Z]@Range[0-9]/" CuNvtx.exe
    

    The kernels wrapped inside push/pop range with matching regex ‘Range[0-9]’ of domain with matching regex ‘Domain[A-Z]’ are profiled.

  • Provide ‘/’ as a prefix to “[” or “]” only for the range part of the config if “[” or “]” is at the start or at the end of the range part, respectively. This is needed so that NCU can distinguish if “[” or “]” is part of the regex or represents the top/bottom of the stack.

    ncu --nvtx --nvtx-include "regex:[0-9]domainA@/[0-9]rangeA,RangeC[0-9/]" CuNvtx.exe
    

    The kernels wrapped inside start/end ranges with matching regex ‘[0-9]rangeA’ and ‘RangeC[0-9]’ of domain with matching regex ‘[0-9]domainA’ are profiled.

  • If any quantifier is part of the domain/range name, you need to use ‘\\’ or ‘\’ as a prefix. For the “$” quantifier, only the ‘\\’ prefix is valid.

• Additional Information

  --nvtx-include "DomainA@RangeA,DomainB@RangeB" //Not a valid config
  

  In a single NVTX configuration, multiple ranges with regard to a single domain can be specified. Mentioning ranges from different domains inside a single NVTX config is not supported.

  --nvtx-include "A_range\[i\]"
  

  Quantifiers @ , [ ] / * , including regular expression quantifiers, can be used in domain/range names using prefix ‘\’. The kernels wrapped inside ‘A_range[i]’ of ‘<default domain>’ in the application are profiled.

  --nvtx-include "A_range"  //Start/End configuration
  --nvtx-inlcude "A_range/" //Push/Pop configuration
  --nvtx-inlcude "A_range]" //Push/Pop configuration
  

  If the domain/range name contains ‘\’, you need to provide ‘\\\\’ in the config.

  Do not use ‘\\\\’ before any quantifier.

  Including/Excluding only single range for Push/Pop configuration without specifying stack frame position ‘[’ or ‘]’, use ‘/’ quantifier at the end.

  --nvtx-include "A_range/*/B range"
  

  The order in which you mention Push/Pop configurations is important. In the above example, ‘A_range’ should be below ‘B range’ in the stack of ranges so that the kernel is profiled.

  NVTX filtering honors cudaProfilerStart() and cudaProfilerStop(). There is no support for ranges with no name.

4.3.8. Config File

Using the --config-file on/off option, parsing parameters from config file can be enabled or disabled.

Using the --config-file-path <path> option, default path and name of config file can be overwritten.

By default, config-file with name config.ncu-cfg is searched in the current working directory, $HOME/.config/NVIDIA Corporation on Linux and %APPDATA%\NVIDIA Corporation\ on Windows. If a valid config file is found, ncu parses the file and initializes any command line parameters to the values set in the file. If the same command line parameter is also set explicitly during the current invocation, the latter takes precedence.

Parameters can be set under various general modes and ncu command line parameters are used to determine which general-mode needs to be parsed from the config file. See the table below for more details.

Command line parameters	General Mode
ncu –mode launch-and-attach CuVectorAddMulti.exe	Launch-and-attach
ncu –mode launch CuVectorAddMulti.exe	Launch
ncu –mode attach	Attach
ncu –list-sets, ncu –list-sections, ncu –list-rules and ncu –list-metrics	List
ncu –query-metrics	Query
ncu -i <MyReport.ncu-rep>	Import

These general modes should be defined in the config file using INI-like syntax as:

[<general-mode>]
<parameter>=<value>
;<comments>

Sample usage

[Launch-and-attach]
-c = 1
--section = LaunchStats, Occupancy
[Import]
--open-in-ui
-c = 1
--section = LaunchStats, Occupancy

From this configuration, ncu will parse parameters set under [Launch-and-attach] block whenever an application is profiled in launch-and-attach mode. In the same manner, parameters set under [Import] block will be parsed whenever a report is imported. Different modes can be clubbed together if there exists a set of parameters which is common to each mode. Sample shown above can be rewritten after clubbing both modes as:

[Launch-and-attach, import]
-c = 1
--section = LaunchStats, Occupancy
[Import]
--open-in-ui

Additional points

• Options like --open-in-ui do not expect any value to be set. These options should not be passed any value.

• Options like --section can be passed multiple times in the command line. These options should be written only once under a general-mode with all required values seperated by comma as shown below. Explicitly setting values for these options will not overwrite the config file values. Instead, all values will be composed together and set to the option.

  [<general-mode>]
  <parameter>=<value1>,<value2>,...
  

4.3.9. Kernel Renaming

In some cases, it gets difficult to distinguish between results using the kernel function or mangled name. However, demangled names can be quite long and complex to understand. To handle such cases, kernel demangled names are auto-simplified to some extent. To see original kernel demangled names, disable kernel renaming using --rename-kernels off option. If a simplified kernel demangled name turns out to be not useful, you can rename it through the configuration file. A kernel renaming configuration file should be a YAML file written in the following format:

-
 - Original: mergeRanksAndIndicesKernel(unsigned int *, unsigned int *, unsigned int, unsigned int, unsigned int)
 - Renamed: Merge Rank Kernel
-
 - Original: void mergeSortSharedKernel<(unsigned int)1>(unsigned int *, unsigned int *, unsigned int *, unsigned int *, unsigned int)
 - Renamed: Merge Sort Kernel

By default, kernel renaming config file with name ncu-kernel-renames.yaml is searched in the similar way as config file is searched.

To avoid manually writing demangled names in the config file, one can use --rename-kernels-export on option to export demangled names from the report to the config file with mappings for renaming them.

Using the --rename-kernels-path <path> option, default path and name of the file used while importing renamed names and exporting can be overwritten.

Note that renamed names can later be used to filter kernels in the report using --kernel-name and --kernel-id options.

4.4. Command Line Options

For long command line options, passing a unique initial substring can be sufficient.

4.4.1. General

Table 1. General Command Line Options

Option	Description	Default
h,help	Show help message
v,version	Show version information
mode	Select the mode of interaction with the target application launch-and-attach: Launch the target application and immediately attach for profiling. launch: Launch the target application and suspend in the first intercepted API call, wait for tool to attach. attach: Attach to a previously launched application to which no other tool is attached.	launch-and-attach
p,port	Base port used for connecting to target applications for --mode launch/attach	49152
max-connections	Maximum number of ports for connecting to target applications	64
config-file	Use config.ncu-cfg config file to set parameters. Searches in the current working directory, in “$HOME/.config/NVIDIA Corporation” on Linux and in “%APPDATA%\NVIDIA Corporation\” on Windows.	on
config-file-path	Override the default path for config file.

4.4.2. Launch

Table 2. Launch Command Line Options

Option	Description	Default
check-exit-code	Check the application exit code and print an error if it is different than 0. If set, --replay-mode application will stop after the first pass if the exit code is not 0.	yes
injection-path-64	Override the default path for the injection libraries. The injection libraries are used by the tools to intercept relevant APIs (like CUDA or NVTX).
preload-library	Prepend a shared library to be loaded by the application before the injection libraries. This option can be given multiple times and the libraries will be loaded in the order they were specified.
call-stack	Enable CPU Call Stack collection.	false
call-stack-type	Set the call stack type(s) that should be collected. More than one type may be specified. Implies –call-stack. Note that Python call stack collection requires CPython version 3.9 or later.	native Examples --call-stack-type native --call-stack-type python
nvtx	Enable NVTX support for tools.	false
target-processes	Select the processes you want to profile. Available modes are: application-only Profile only the root application process. all Profile the application and all its child processes.	all
target-processes-filter	Set the comma separated expressions to filter which processes are profiled. <process name> Set the exact process name to include for profiling. regex:<expression> Set the regex to filter matching process name profiling. On shells that recognize regular expression symbols as special characters (e.g. Linux bash), the expression needs to be escaped with quotes, e.g. --target-processes-filter regex:".*Process". When using regex:, the expression must not include any commas. exclude:<process name> Set the exact process name to exclude for profiling. exclude-tree:<process name> Set the exact process name to exclude for profiling and further process tracking. None of its child processes will be profiled, even if they match a positive filter. This option is not available on Windows. The executable name part of the process will be considered in the match. Processing of filters stops at the first match. If any positive filter is specified, no process that is not matching a positive filter is profiled.	Examples --target-processes-filter MatrixMul Filter all processes having executable name exactly as “MatrixMul”. --target-processes-filter regex:MatrixFilter all processes that include the string “Matrix” in their executable name, e.g. “MatrixMul” and “MatrixAdd”. --target-processes-filter MatrixMul,MatrixAddFilter all processes having executable name exactly as “MatrixMul” or “MatrixAdd”. --target-processes-filter exclude:MatrixMul.exe Exclude only “MatrixMul.exe”. --target-processes-filter exclude-tree:ChildLauncher,ParentProcess Exclude “ChildLauncher” and all its sub-processes. Include (only) “ParentProcess”, but not if it’s a child of “ChildLauncher”.
support-32bit	Support profiling processes launched from 32-bit applications. This option is only available on x86_64 Linux. On Windows, tracking 32-bit applications is enabled by default.	no
null-stdin	Launch the application with ‘/dev/null’ as its standard input. This avoids applications reading from standard input being stopped by SIGTTIN signals and hanging when running as backgrounded processes.	false

4.4.3. Attach

Table 3. Attach Command Line Options

Option	Description	Default
hostname	Set the hostname or IP address for connecting to the machine on which the target application is running. When attaching to a local target application, use 127.0.0.1.	127.0.0.1

4.4.4. Profile

Table 4. Profile Command Line Options

Option	Description	Default/Examples
devices	List the GPU devices to enable profiling on, separated by comma. 1	All devices Examples --devices 0,2
filter-mode	Set the filtering mode for kernel launches. Available modes: global: Apply provided launch filters on kernel launches collectively. per-gpu: Apply provided launch filters on kernel launches separately on each device. Effective launch filters for this mode are --launch-count and --launch-skip per-launch-config: Apply kernel filters and launch filters on kernel launches separately for each GPU launch parameter i.e. Grid Size, Block Size and Shared Memory.	global
kernel-id	Set the identifier to use for matching kernels. If the kernel does not match the identifier, it will be ignored for profiling. The identifier must be of the following format: context-id:stream-id:[name-operator:]kernel-name:invocation-nr context-id is the CUDA context ID or regular expression of context id, NVTX name. stream-id is the CUDA stream ID or regular expression of stream id, NVTX name. name-operator is an optional operator to kernel-name. Currently, regex is the only supported operator. kernel-name is the expression to match the kernel name. By default, this is a full, literal match to what is specified by --kernel-name-base. When specifying the optional regex name operator, this is a partial regular expression match to what is specified by --kernel-name-base. invocation-nr is the N’th invocation of matching kernel filter i.e. ctx id, stream id, kernel name, grid dimensions, block dimensions and shared memory bytes are all considered for invocation count. If ctx id or stream id is not provided then respective id is not considered for invocation count. If Multiple invocations can also be specified using regular expressions. Multiple invocations can also be specified using regular expressions. If the context/stream ID is a positive number, it will be strictly matched against the CUDA context/stream ID. Otherwise it will be treated as a regular expression and matched against the context/stream name specified using the NVTX library. 1	Examples --kernel-id ::foo:2 For kernel “foo”, match the second invocation. --kernel-id :::".*5|3" For all kernels, match the third invocation, and all for which the invocation number ends in “5”. --kernel-id ::regex:^.*foo$: Match all kernels ending in “foo”. --kernel-id ::regex:^(?!foo): Match all kernels except those starting with “foo”. Note that depending on your OS and shell, ` you might need to quote the expression, e.g. using single quotes in Linux bash: --kernel-id ::regex:'^(?!foo)': --kernel-id 1|5:2::7 Match all seventh kernel invocations of kernels lauched from context 1 stream 2, and context 5 stream 2.
k,kernel-name	Set the expression to use when matching kernel names. <kernel name> Set the kernel name for an exact match. regex:<expression> Set the regex to use for matching the kernel name. On shells that recognize regular expression symbols as special characters (e.g. Linux bash), the expression needs to be escaped with quotes, e.g. --kernel-name regex:".*Foo". If the kernel name or the provided expression do not match, it will be ignored for profiling. 1	Examples -k foo Match all kernels named exactly “foo”. -k regex:foo Match all kernels that include the string “foo”, e.g. “foo” and “fooBar”. -k regex:"foo|bar" Match all kernels including the strings “foo” or “bar”, e.g. “foo”, “foobar”, “_bar2”.
kernel-name-base	Set the basis for --kernel-name, and --kernel-id kernel-name. 1 Options are: function: Function name without parameters, templates etc. e.g. dmatrixmul demangled: Demangled function name, including parameters, templates, etc. e.g. dmatrixmul(float*,int,int). Use Kernel Renaming to rename the demangled name. mangled: Mangled function name. e.g. _Z10dmatrixmulPfiiS_iiS_	function
rename-kernels	Perform simplification on the kernel demangled names. Rename demangled names using a config file. See Kernel Renaming for more details.	on
rename-kernels-export	Export demangled names from the report to a new file and specify mappings for renaming them. Use --rename-kernels-path option to specify the export file path.	off
rename-kernels-path	Override the default path of the configuration file which should be used while importing renamed kernels or exporting demangled names. Only valid while using --rename-kernels or --rename-kernels-export.
c,launch-count	Limit the number of profiled kernel launches. The count is only incremented for launches that match the kernel filters.1
s,launch-skip	Set the number of kernel launches to skip before starting to profile kernels. The number takes into account only launches that match the kernel filters. 1	0
launch-skip-before-match	Set the number of kernel launches to skip before starting to profile. The count is incremented for all launches, regardless of the kernel filters. 1	0
range-filter	Filter to profile specified instance(s) of matching NVTX ranges or start/stop ranges created through cu(da)ProfilerStart/Stop APIs. Specify in format [yes/no/on/off]:[start/stop range instance(s)]:[NVTX range instance(s)] [yes/no/on/off] : default is ‘no/off’. If set to ‘yes/on’ then NVTX range numbering starts from 1 inside every start/stop range. provide numbers in regex form e.g, [2-4] or 2|3|4 to profile 2nd, 3rd and 4th instance of the matching range. NVTX range numbers will be counted for matching range provided using –nvtx-include.	Examples --range-filter :2:3 --nvtx-include A/ Match 2nd start/stop range and also 3rd NVTX push/pop range A in the app. --range-filter yes:2:3 --nvtx-include A/ Match 3rd NVTX push/pop range A from 2nd start/stop range.
kill	Terminate the target application when the requested –launch-count was profiled. Allowed values: on/off yes/no	no
replay-mode	Mechanism used for replaying a kernel launch multiple times to collect all requested profiling data: kernel: Replay individual kernel launches “transparently” during the execution of the application. See Kernel Replay for more details. application: Relaunch the entire application multiple times. Requires deterministic program execution. See Application Replay for more details. range: Replay ranges of CUDA API calls and kernel launches “transparently” during the execution of the application. Ranges must be defined using cu(da)ProfilerStart/Stop API pairs or NVTX expressions. See Range Replay for more details. app-range: Profile ranges without API capture by relaunching the entire application multiple times. Requires deterministic program execution. Ranges must be defined using cu(da)ProfilerStart/Stop API pairs or NVTX expressions. See Application Range Replay for more details.	kernel
app-replay-buffer	Application replay buffer location. file: Replay pass data is buffered in a temporary file. The report is created after profiling completed. This mode is more scalable, as the amount of required memory does not scale with the number of profiled kernels. memory: Replay pass data is buffered in memory, and the report is created while profiling. This mode can result in better performance if the filesystem is slow, but the amount of required memory scales with the number of profiled kernels.	file
app-replay-match	Application replay kernel matching strategy. For all options, kernels are matched on a per-process and per-device (GPU) basis. Below options are used to configure the applied strategy in more detail. name: Kernels are matched in the following order: 1. (mangled) name, 2. order of execution grid: Kernels are matched in the following order: 1. (mangled) name, 2. CUDA grid/block size, 3. order of execution all: Kernels are matched in the following order: 1. (mangled) name, 2. CUDA grid/block size, 3. CUDA context ID, 4. CUDA stream ID, 5. order of execution	grid
app-replay-mode	Application replay kernel matching mode: strict: Requires all kernels to match across all replay passes. relaxed: Produces results only for kernels that could be matched across replay passes.	strict
range-replay-options	Range replay options, separated by comma. Below options are supported: enable-greedy-sync Insert ctx sync for applicable deferred APIs during capture. disable-host-restore Disable restoring device-written host allocations.	none
graph-profiling	CUDA graph profiling mode: node Profile individual kernel nodes as regular CUDA kernels. graph Profile entire graphs as one workload (but disable profiling of individual graph kernel nodes). See the Kernel Profiling Guide for more information on this mode.	node
list-sets	List all section sets found in the searched section folders and exit. For each set, the associated sections are shown, as well as the estimated number of metrics collected as part of this set. This number can be used as an estimate of the relative profiling overhead per kernel launch of this set.
set	Identifier of section set to collect. If not specified, the basic set is collected. The full set of sections can be collected with --set full.	If no --set option is given, the basic set is collected. If not specified and --section or --metrics are used, no sets are collected. Use --list-sets to see which set is the default.
list-sections	List all sections found in the searched section folders and exit.
section	Add a section identifier to collect in one of the following ways: <section identifier> Set the section identifier for an exact match. regex:<expression> Regular expression allows matching full section identifier. For example, .*Stats, matches all sections ending with ‘Stats’. On shells that recognize regular expression symbols as special characters (e.g. Linux bash), the expression needs to be escaped with quotes, e.g. --section "regex:.*Stats". This option is ignored when used with --import and --page raw or --page source. 1	If no --section options are given, the sections associated with the basic set are collected. If no sets are found, all sections are collected.
section-folder	Add a non-recursive search path for .section and .py rule files. Section files in this folder will be made available to the --section option. Individual files from directories including an .ncu-ignore file are ignored.	If no --section-folder options are given, the sections folder is added by default.
section-folder-recursive	Add a recursive search path for .section and .py rule files. Section files in this folder and all folders below will be made available to the --section option. Individual files from directories including an .ncu-ignore file are ignored.	If no --section-folder options are given, the sections folder is added by default.
list-rules	List all rules found in the searched section folders and exit.
apply-rules	Apply active and applicable rules to each profiling result. Use --rule to limit which rules to apply. Allowed values: on/off yes/no	yes
rule	Add a rule identifier to apply. Implies --apply-rules yes.	If no --rule options are given, all applicable rules in the sections folder are applied.
import-source	If available from -lineinfo, correlated CUDA source files are permanently imported into the report. Allowed values: on/off yes/no Use --source-folders option to provide missing source files.	no
source-folders	Add comma separated recursive search paths for missing CUDA source files to import into the report.
list-metrics	List all metrics collected from active sections. If the list of active sections is restricted using the --section option, only metrics from those sections will be listed.
query-metrics	Query available metrics for the devices on system. Use --devices and --chips to filter which devices to query. Note that by default, listed metric names need to be appended a valid suffix in order for them to become valid metrics. See --query-metrics-mode for how to get the list of valid suffixes, or check the Kernel Profiling Guide.
query-metrics-mode	Set the mode for querying metrics. Implies --query-metrics. Available modes: base: Only the base names of the metrics. suffix: Suffix names for the base metrics. This gives the list of all metrics derived from the base metrics. Use --metrics to specify the base metrics to query. all: Full names for all metrics. This gives the list of all base metrics and their suffix metrics.	base
query-metrics-collection	Set which metric collection kind to query. Implies --query-metrics. Available collections: profiling: Query metrics available for profiling. pmsampling: Query metrics available for PM sampling.	profiling
metrics	Specify all metrics to be profiled, separated by comma. If no --section options are given, only the temporary section containing all metrics listed using this option is collected. If --section options are given in addition to --metrics, all metrics from those sections and from --metrics are collected. Names passed to this option support the following prefixes: regex:<expression> expands to all metrics that partially match the expression. Enclose the regular expression in ^…$ to force a full match. group:<name> lists all metrics of the metric group with that name. See section files for valid group names. breakdown:<metric> expands to the input metrics of the high-level throughput metric. pmsampling:<metric> collects the metric using PM sampling. Only single-pass metrics that don’t require SASS-patching (_sass_) are supported. Using this prefix adds a timeline element to the report’s details page. Combining multiple prefixes is not supported. If a metric requires a suffix to be valid, and neither regex: nor group: are used, this option automatically expands the name to all available first-level sub-metrics. When importing a report, :group and :breakdown are not supported. When using regex:, the expression must not include any commas. 1
disable-extra-suffixes	Disable the collection of extra suffixes (avg, min, max, sum) for all metrics. Only collect what is explicity specified.
list-chips	List all supported chips that can be used with --chips.
chips	Specify the chips for querying metrics, separated by comma.	Examples --chips gv100,tu102
profile-from-start	Set if application should be profiled from its start. Allowed values: on/off yes/no	yes
disable-profiler-start-stop	Disable profiler start/stop. When enabled, cu(da)ProfilerStart/Stop API calls are ignored.
quiet	Suppress all profiling output.
verbose	Make profiler output more verbose.
cache-control	Control the behavior of the GPU caches during profiling. Allowed values: all: All GPU caches are flushed before each kernel replay iteration during profiling. While metric values in the execution environment of the application might be slightly different without invalidating the caches, this mode offers the most reproducible metric results across the replay passes and also across multiple runs of the target application. none: No GPU caches are flushed during profiling. This can improve performance and better replicates the application behavior if only a single kernel replay pass is necessary for metric collection. However, some metric results will vary depending on prior GPU work, and between replay iterations. This can lead to inconsistent and out-of-bounds metric values.	all
clock-control	Control the behavior of the GPU clocks during profiling. Allowed values: base: GPC and memory clocks are locked to their respective base frequency during profiling. This has no impact on thermal throttling. Note that actual clocks might still vary, depending on the level of driver support for this feature. As an alternative, use nvidia-smi to lock the clocks externally and set this option to none. none: No GPC or memory frequencies are changed during profiling. reset: Reset GPC and memory clocks for all or the selected devices and exit. Use if a previous, killed execution of ncu left the GPU clocks in a locked state.	base
nvtx-include	Adds an include statement to the NVTX filter, which allows selecting kernels to profile based on NVTX ranges. 1
nvtx-exclude	Adds an exclude statement to the NVTX filter, which allows selecting kernels to profile based on NVTX ranges. 1

1(1,2,3,4,5,6,7,8,9,10,11)

This filtering option is available when using --import.

4.4.5. PM Sampling

These options apply to PM sampling. See here for options used in warp state sampling.

Table 5. PM Sampling Command Line Options

Option	Description	Default
pm-sampling-interval	Set the PM sampling interval in cycles or ns (depending on the architecture), or determine dynamically when 0.	0 (auto)
pm-sampling-buffer-size	Set the size of the device-sided allocation for PM sampling in bytes, or determine dynamically when 0.	0 (auto)
pm-sampling-max-passes	Set the maximum number of passes used for PM sampling, or determine dynamically when 0.	0 (auto)

4.4.6. Warp Sampling

Table 6. Warp Sampling Command Line Options

Option	Description	Default
warp-sampling-interval	Set the sampling period in the range of [0..31]. The actual frequency is 2 ^ (5 value) cycles. If set to ‘auto’, the profiler tries to automatically determine a high sampling frequency without skipping samples or overflowing the output buffer.	auto
warp-sampling-max-passes	Set maximum number of passes used for sampling (see the Kernel Profiling Guide for more details on profiling overhead).	5
warp-sampling-buffer-size	Set the size of the device-sided allocation for samples in bytes.	32*1024*1024

4.4.7. File

Table 7. File Command Line Options

Option	Description	Default
log-file	Send all tool output to the specified file, or one of the standard channels. The file will be overwritten. If the file doesn’t exist, a new one will be created.”stdout” as the whole file name indicates standard output channel (stdout). “stderr” as the whole file name indicates standard error channel (stderr).”	If --log-file is not set , profile results will be printed on the console.
o,export	Set the output file for writing the profile report. If not set, a temporary file will be used which is removed afterwards. Use with --import option to save filtered results. See Filtered Profile Export for more details. The specified name supports macro expansion. See File Macros for more details.	If --export is set and no --page option is given, no profile results will be printed on the console.
f,force-overwrite	Force overwriting all output files.	By default, the profiler won’t overwrite existing output files and show an error instead.
i,import	Set the input file for reading the profile results.
open-in-ui	Open report in UI instead of showing result on terminal. (Only available on host platforms)
section-folder-restore	Restores stock files to the default section folder or the folder specified by an accompanying –section-folder option. If the operation will overwrite modified files then the –force-overwrite option is required.

4.4.8. Console Output

Table 8. Console Output Command Line Options

Option	Description	Default
csv	Use comma-separated values as console output. Implies –print-units base by default.
page	Select the report page to print console output for. Available pages are: details Show results grouped as sections, include rule results. Some metrics that are collected by default (e.g. device attributes) are omitted if not specified explicitly in any section or using --metrics. raw Show all collected metrics by kernel launch. source Show source. See --print-source to select the source view. session Show launch settings, session info, process info and device attributes.	details. If no --page option is given and --export is set, no results are printed to the console output.
print-source	Select the source view: sass Show SASS (assembly) instructions for each kernel launch. ptx Show PTX source of every cubin whose at least one kernel is profiled. cuda Show entire CUDA-C source file which has kernel code as per kernel launch. CLI shows CUDA source only if file exists on the host machine. cuda,sass Show SASS CUDA-C source correlation for each kernel launch. CLI shows CUDA source only if file exists on the host machine. Metric correlation with source is available in sass, and cuda,sass source view. Metrics specified with --metrics and specified section file with --section are correlated. Consider restricting the number of selected metrics such that values fit into a single output row.	sass
resolve-source-file	Resolve CUDA source file in the --page source output. Provide comma separated files full path.
print-details	Select which part of a section should be shown in the details page output: header Show all metrics from header of the section. body Show all metrics from body of the section. all Show all metrics from the section. Replaces deprecated option --details-all.	header
print-metric-name	Select one of the option to show it in the Metric Name column: label Show metric label. name Show metric name. label-name Show both metric label and metric name.	label
print-units	Select the mode for scaling of metric units. Available modes are: auto Show all metrics automatically scaled to the most fitting order of magnitude. base Show all metrics in their base unit.	auto
print-metric-attribution	Show the attribution level for metrics of Green Context results.	false
print-fp	Show all numeric metrics in the console output as floating point numbers.	false
print-kernel-base	Set the basis for kernel name output. See --kernel-regex-base for options.	demangled
print-metric-instances	Set output mode for metrics with instance values: none Only show GPU aggregate value. values Show GPU aggregate followed by all instance values. details Show aggregate value, followed by correlation IDs and instance values	none
print-nvtx-rename	Select how NVTX should be used for renaming: none Don’t use NVTX for renaming. kernel Rename kernels with the most recent enclosing NVTX push/pop range.	none
print-rule-details	Print additional details for rule results, such as the triggering metrics. Currently has no effect in CSV mode.	false
print-summary	Select the summary output mode. Available modes are: none No summary. per-gpu Summary for each GPU. per-kernel Summary for each kernel type. per-nvtx Summary for each NVTX context.	none

4.4.9. Response File

Response files can be specified by adding @FileName to the command line. The file name must immediately follow the @ character. The content of each response file is inserted in place of the corresponding response file option.

4.4.10. File Macros

The file name specified with option -o or --export supports the following macro expansions. Occurrences of these macros in the report file name are replaced by the corresponding character sequence. If not specified otherwise, the macros cannot be used as part of the file path.

Table 9. Macro Expansions

Macro	Description
%h	Expands to the host name of the machine on which the command line profiler is running.
%q{ENV_NAME}	Expands to the content of the variable with the given name ENV_NAME from the environment of the command line profiler.
%p	Expands to the process ID of the command line profiler.
%i	Expands to the lowest unused positive integer number that guarantees the resulting file name is not yet used. This macro can only be used once in the output file name.
%%	Expands to a single % character in the output file name. This macro can be used in the file path and the file name.

4.5. Environment Variables

The following environment variables can be set before launching NVIDIA Nsight Compute CLI, or the UI, respectively.

Table 10. Environment Variables

Name	Description	Default/Values
NV_COMPUTE_PROFILER_DISABLE_STOCK_FILE_DEPLOYMENT	Disable file deployment to the versioned Sections directory, using section and rule files from the stock directory within the installation instead. By default, the versioned directory from the user’s documents folder is used to ensure that any potential user updates are taken into account. Only supported in the NVIDIA Nsight Compute CLI.	Default: unset Set to “1” to disable deployment.
NV_COMPUTE_PROFILER_LOCAL_CONNECTION_OVERRIDE	Override the default local connection mechanism between frontend and profiled target processes. The default mechanism is platform-dependent. This should only be used if there are connection problems between frontend and target processes in a local launch.	Default: unset (use default mechanism) Set to “uds” to use Unix Domain Socket connections (available on Posix platforms, only). Set to “tcp” to use TCP (available on all platforms). Set to “named-pipes” to use Windows Named Pipes (available on Windows, only).
NV_COMPUTE_PROFILER_DISABLE_SW_PRE_PASS	Disable the instruction-level software (SW) metric pre-pass. When collecting SW-patched metrics, such as inst_executed, the pre-pass is used to determine which functions are executed as part of the kernel and should be patched. This requires a separate replay pass, and if only instruction-level SW metrics are to be collected, prevents single-pass data collection. Disabling the pre-pass can improve performance if memory save-and-restore is undesirable and application replay is not possible.	Default: unset (use pre-pass when applicable) Set to “1” to disable pre-pass.

4.6. Nvprof Transition Guide

This guide provides tips for moving from nvprof to NVIDIA Nsight Compute CLI. NVIDIA Nsight Compute CLI tries to provide as much feature and usage parity as possible with nvprof, but some features are now covered by different tools and some command line options have changed their name or meaning.

4.6.1. Trace

• GPU and API trace

  NVIDIA Nsight Compute CLI does not support any form of tracing GPU or API activities. This functionality is covered by NVIDIA Nsight Systems.

4.6.2. Metric Collection

• Finding available metrics

  For nvprof, you can use --query-metrics to see the list of metrics available for the current devices on your machine. You can also use --devices to filter which local devices to query. For NVIDIA Nsight Compute CLI, this functionality is the same. However, in addition, you can combine --query-metrics with --chip [chipname] to query the available metrics for any chip, not only the ones in your present CUDA devices.

  Note that metric names have changed between nvprof and NVIDIA Nsight Compute CLI and metric names also differ between chips after (and including) GV100 and those before. See Metric Comparison for a comparison of nvprof and NVIDIA Nsight Compute metric names.

  On Volta and newer GPUs, most metrics are named using a base name and various suffixes, e.g. sm__throughput.avg.pct_of_peak_sustained_elapsed. The base name is sm__throughput and the suffix is avg.pct_of_peak_sustained_elapsed. This is because most metrics follow the same structure and have the same set of suffixes. You need to pass the full name to NVIDIA Nsight Compute when selecting a metric for profiling.

  To reduce the number of metrics shown for Volta and newer GPUs when using --query-metrics, by default only the base names are shown. Use --query-metrics-mode suffix --metrics <metrics list> to see the full names for the chosen metrics. Use --query-metrics-mode all to see all metrics with their full name directly.

• Selecting which metrics to collect

  In both nvprof and NVIDIA Nsight Compute CLI, you can specify a comma-separated list of metric names to the --metrics option. While nvprof would allow you to collect either a list or all metrics, in NVIDIA Nsight Compute CLI you can use regular expressions to select a more fine-granular subset of all available metrics. For example, you can use --metrics "regex:.*" to collect all metrics, or --metrics "regex:smsp__cycles_elapsed" to collect all “smsp__cycles_elapsed” metrics.

• Selecting which events to collect

  You cannot collect any events in NVIDIA Nsight Compute CLI.

• Selecting which section to collect

  In nvprof, you can either collect individual metrics or events, or a pre-configured set (all, analysis-metrics). NVIDIA Nsight Compute CLI adds the concept of a section. A section is a file that describes which metrics to collect for which GPU architecture, or architecture range. Furthermore, it defines how those metrics will be shown in both the command line output or the user interface. This includes structuring in tables, charts, histograms etc.

  NVIDIA Nsight Compute CLI comes with a set of pre-defined sections, located in the sections directory. You can inspect, modify or extend those, as well as add new ones, e.g. to easily collect recurring metric sets. Each section specifies a unique section identifier, and there must not be two sections with the same identifier in the search path.

  By default, the sections associated with the basic section set are collected. You can select one or more individual sections using the --section [section identifier] option one or more times. If no --section option is given, but --metrics is used, no sections will be collected.

• Selecting which section set to collect

  In nvprof, you can either collect individual metrics or events, or a pre-configured set (all, analysis-metrics). NVIDIA Nsight Compute CLI adds the concept of section sets. A section set defines a group of sections to collect together, in order to achieve different profiling overheads, depending on the required analysis level of detail.

  If no other options are selected, the basic section set is collected. You can select one or more sets using the --set [set identifier] option one or more times. If no --set option is given, but --section or --metrics is used, no sets will be collected.

4.6.3. Metric Comparison

NVIDIA Nsight Compute uses two groups of metrics, depending on which GPU architecture is profiled. For nvprof metrics, the following table lists the equivalent metrics in NVIDIA Nsight Compute, if available. For a detailed explanation of the structuring of PerfWorks metrics, see Metrics Structure.

Metrics starting with sm__& are collected per-SM. Metrics starting with *smsp__ are collected per-SM subpartition. However, all corresponding nvprof events are collected per-SM, only. Check the Metrics Guide for more details on these terms.

Table 11. Metrics Mapping Table from CUPTI to PerfWorks

nvprof Metric	PerfWorks Metric or Formula (>= SM 7.0)
achieved_occupancy	sm__warps_active.avg.pct_of_peak_sustained_active
atomic_transactions	l1tex__t_set_accesses_pipe_lsu_mem_global_op_atom.sum l1tex__t_set_accesses_pipe_lsu_mem_global_op_red.sum
atomic_transactions_per_request	(l1tex__t_sectors_pipe_lsu_mem_global_op_atom.sum l1tex__t_sectors_pipe_lsu_mem_global_op_red.sum) / (l1tex__t_requests_pipe_lsu_mem_global_op_atom.sum l1tex__t_requests_pipe_lsu_mem_global_op_red.sum)
branch_efficiency	smsp__sass_average_branch_targets_threads_uniform.pct
cf_executed	smsp__inst_executed_pipe_cbu.sum smsp__inst_executed_pipe_adu.sum
cf_fu_utilization	n/a
cf_issued	n/a
double_precision_fu_utilization	smsp__inst_executed_pipe_fp64.avg.pct_of_peak_sustained_active
dram_read_bytes	dram__bytes_read.sum
dram_read_throughput	dram__bytes_read.sum.per_second
dram_read_transactions	dram__sectors_read.sum
dram_utilization	dram__throughput.avg.pct_of_peak_sustained_elapsed
dram_write_bytes	dram__bytes_write.sum
dram_write_throughput	dram__bytes_write.sum.per_second
dram_write_transactions	dram__sectors_write.sum
eligible_warps_per_cycle	smsp__warps_eligible.sum.per_cycle_active
flop_count_dp	smsp__sass_thread_inst_executed_op_dadd_pred_on.sum smsp__sass_thread_inst_executed_op_dmul_pred_on.sum smsp__sass_thread_inst_executed_op_dfma_pred_on.sum * 2
flop_count_dp_add	smsp__sass_thread_inst_executed_op_dadd_pred_on.sum
flop_count_dp_fma	smsp__sass_thread_inst_executed_op_dfma_pred_on.sum
flop_count_dp_mul	smsp__sass_thread_inst_executed_op_dmul_pred_on.sum
flop_count_hp	smsp__sass_thread_inst_executed_op_hadd_pred_on.sum smsp__sass_thread_inst_executed_op_hmul_pred_on.sum smsp__sass_thread_inst_executed_op_hfma_pred_on.sum * 2
flop_count_hp_add	smsp__sass_thread_inst_executed_op_hadd_pred_on.sum
flop_count_hp_fma	smsp__sass_thread_inst_executed_op_hfma_pred_on.sum
flop_count_hp_mul	smsp__sass_thread_inst_executed_op_hmul_pred_on.sum
flop_count_sp	smsp__sass_thread_inst_executed_op_fadd_pred_on.sum smsp__sass_thread_inst_executed_op_fmul_pred_on.sum smsp__sass_thread_inst_executed_op_ffma_pred_on.sum * 2
flop_count_sp_add	smsp__sass_thread_inst_executed_op_fadd_pred_on.sum
flop_count_sp_fma	smsp__sass_thread_inst_executed_op_ffma_pred_on.sum
flop_count_sp_mul	smsp__sass_thread_inst_executed_op_fmul_pred_on.sum
flop_count_sp_special	n/a
flop_dp_efficiency	smsp__sass_thread_inst_executed_ops_dadd_dmul_dfma_pred_on.avg.pct_of_peak_sustained_elapsed
flop_hp_efficiency	smsp__sass_thread_inst_executed_ops_hadd_hmul_hfma_pred_on.avg.pct_of_peak_sustained_elapsed
flop_sp_efficiency	smsp__sass_thread_inst_executed_ops_fadd_fmul_ffma_pred_on.avg.pct_of_peak_sustained_elapsed
gld_efficiency	smsp__sass_average_data_bytes_per_sector_mem_global_op_ld.pct
gld_requested_throughput	n/a
gld_throughput	l1tex__t_bytes_pipe_lsu_mem_global_op_ld.sum.per_second
gld_transactions	l1tex__t_sectors_pipe_lsu_mem_global_op_ld.sum
gld_transactions_per_request	l1tex__average_t_sectors_per_request_pipe_lsu_mem_global_op_ld.ratio
global_atomic_requests	l1tex__t_requests_pipe_lsu_mem_global_op_atom.sum
global_hit_rate	(l1tex__t_sectors_pipe_lsu_mem_global_op_ld_lookup_hit.sum l1tex__t_sectors_pipe_lsu_mem_global_op_st_lookup_hit.sum l1tex__t_sectors_pipe_lsu_mem_global_op_red_lookup_hit.sum l1tex__t_sectors_pipe_lsu_mem_global_op_atom_lookup_hit.sum) / (l1tex__t_sectors_pipe_lsu_mem_global_op_ld.sum l1tex__t_sectors_pipe_lsu_mem_global_op_st.sum l1tex__t_sectors_pipe_lsu_mem_global_op_red.sum l1tex__t_sectors_pipe_lsu_mem_global_op_atom.sum)
global_load_requests	l1tex__t_requests_pipe_lsu_mem_global_op_ld.sum
global_reduction_requests	l1tex__t_requests_pipe_lsu_mem_global_op_red.sum
global_store_requests	l1tex__t_requests_pipe_lsu_mem_global_op_st.sum
gst_efficiency	smsp__sass_average_data_bytes_per_sector_mem_global_op_st.pct
gst_requested_throughput	n/a
gst_throughput	l1tex__t_bytes_pipe_lsu_mem_global_op_st.sum.per_second
gst_transactions	l1tex__t_sectors_pipe_lsu_mem_global_op_st.sum
gst_transactions_per_request	l1tex__average_t_sectors_per_request_pipe_lsu_mem_global_op_st.ratio
half_precision_fu_utilization	smsp__inst_executed_pipe_fp16.avg.pct_of_peak_sustained_active
inst_bit_convert	smsp__sass_thread_inst_executed_op_conversion_pred_on.sum
inst_compute_ld_st	smsp__sass_thread_inst_executed_op_memory_pred_on.sum
inst_control	smsp__sass_thread_inst_executed_op_control_pred_on.sum
inst_executed	smsp__inst_executed.sum
inst_executed_global_atomics	smsp__sass_inst_executed_op_global_atom.sum
inst_executed_global_loads	smsp__inst_executed_op_global_ld.sum
inst_executed_global_reductions	smsp__inst_executed_op_global_red.sum
inst_executed_global_stores	smsp__inst_executed_op_global_st.sum
inst_executed_local_loads	smsp__inst_executed_op_local_ld.sum
inst_executed_local_stores	smsp__inst_executed_op_local_st.sum
inst_executed_shared_atomics	smsp__inst_executed_op_shared_atom.sum smsp__inst_executed_op_shared_atom_dot_alu.sum smsp__inst_executed_op_shared_atom_dot_cas.sum
inst_executed_shared_loads	smsp__inst_executed_op_shared_ld.sum
inst_executed_shared_stores	smsp__inst_executed_op_shared_st.sum
inst_executed_surface_atomics	smsp__inst_executed_op_surface_atom.sum
inst_executed_surface_loads	smsp__inst_executed_op_surface_ld.sum smsp__inst_executed_op_shared_atom_dot_alu.sum smsp__inst_executed_op_shared_atom_dot_cas.sum
inst_executed_surface_reductions	smsp__inst_executed_op_surface_red.sum
inst_executed_surface_stores	smsp__inst_executed_op_surface_st.sum
inst_executed_tex_ops	smsp__inst_executed_op_texture.sum
inst_fp_16	smsp__sass_thread_inst_executed_op_fp16_pred_on.sum
inst_fp_32	smsp__sass_thread_inst_executed_op_fp32_pred_on.sum
inst_fp_64	smsp__sass_thread_inst_executed_op_fp64_pred_on.sum
inst_integer	smsp__sass_thread_inst_executed_op_integer_pred_on.sum
inst_inter_thread_communication	smsp__sass_thread_inst_executed_op_inter_thread_communication_pred_on.sum
inst_issued	smsp__inst_issued.sum
inst_misc	smsp__sass_thread_inst_executed_op_misc_pred_on.sum
inst_per_warp	smsp__average_inst_executed_per_warp.ratio
inst_replay_overhead	n/a
ipc	smsp__inst_executed.avg.per_cycle_active
issue_slot_utilization	smsp__issue_active.avg.pct_of_peak_sustained_active
issue_slots	smsp__inst_issued.sum
issued_ipc	smsp__inst_issued.avg.per_cycle_active
l2_atomic_throughput	2 * ( lts__t_sectors_op_atom.sum.per_second lts__t_sectors_op_red.sum.per_second )
l2_atomic_transactions	2 * ( lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum )
l2_global_atomic_store_bytes	lts__t_bytes_equiv_l1sectormiss_pipe_lsu_mem_global_op_atom.sum
l2_global_load_bytes	lts__t_bytes_equiv_l1sectormiss_pipe_lsu_mem_global_op_ld.sum
l2_local_global_store_bytes	lts__t_bytes_equiv_l1sectormiss_pipe_lsu_mem_local_op_st.sum lts__t_bytes_equiv_l1sectormiss_pipe_lsu_mem_global_op_st.sum
l2_local_load_bytes	lts__t_bytes_equiv_l1sectormiss_pipe_lsu_mem_local_op_ld.sum
l2_read_throughput	lts__t_sectors_op_read.sum.per_second lts__t_sectors_op_atom.sum.per_second lts__t_sectors_op_red.sum.per_second 2
l2_read_transactions	lts__t_sectors_op_read.sum lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum 2
l2_surface_load_bytes	lts__t_bytes_equiv_l1sectormiss_pipe_tex_mem_surface_op_ld.sum
l2_surface_store_bytes	lts__t_bytes_equiv_l1sectormiss_pipe_tex_mem_surface_op_st.sum
l2_tex_hit_rate	lts__t_sector_hit_rate.pct
l2_tex_read_hit_rate	lts__t_sector_op_read_hit_rate.pct
l2_tex_read_throughput	lts__t_sectors_srcunit_tex_op_read.sum.per_second
l2_tex_read_transactions	lts__t_sectors_srcunit_tex_op_read.sum
l2_tex_write_hit_rate	lts__t_sector_op_write_hit_rate.pct
l2_tex_write_throughput	lts__t_sectors_srcunit_tex_op_write.sum.per_second
l2_tex_write_transactions	lts__t_sectors_srcunit_tex_op_write.sum
l2_utilization	lts__t_sectors.avg.pct_of_peak_sustained_elapsed
l2_write_throughput	lts__t_sectors_op_write.sum.per_second lts__t_sectors_op_atom.sum.per_second lts__t_sectors_op_red.sum.per_second
l2_write_transactions	lts__t_sectors_op_write.sum lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum
ldst_executed	n/a
ldst_fu_utilization	smsp__inst_executed_pipe_lsu.avg.pct_of_peak_sustained_active
ldst_issued	n/a
local_hit_rate	n/a
local_load_requests	l1tex__t_requests_pipe_lsu_mem_local_op_ld.sum
local_load_throughput	l1tex__t_bytes_pipe_lsu_mem_local_op_ld.sum.per_second
local_load_transactions	l1tex__t_sectors_pipe_lsu_mem_local_op_ld.sum
local_load_transactions_per_request	l1tex__average_t_sectors_per_request_pipe_lsu_mem_local_op_ld.ratio
local_memory_overhead	n/a
local_store_requests	l1tex__t_requests_pipe_lsu_mem_local_op_st.sum
local_store_throughput	l1tex__t_sectors_pipe_lsu_mem_local_op_st.sum.per_second
local_store_transactions	l1tex__t_sectors_pipe_lsu_mem_local_op_st.sum
local_store_transactions_per_request	l1tex__average_t_sectors_per_request_pipe_lsu_mem_local_op_st.ratio
nvlink_data_receive_efficiency	n/a
nvlink_data_transmission_efficiency	n/a
nvlink_overhead_data_received	(nvlrx__bytes_data_protocol.sum / nvlrx__bytes.sum) * 100
nvlink_overhead_data_transmitted	(nvltx__bytes_data_protocol.sum / nvltx__bytes.sum) * 100
nvlink_receive_throughput	nvlrx__bytes.sum.per_second
nvlink_total_data_received	nvlrx__bytes.sum
nvlink_total_data_transmitted	nvltx__bytes.sum
nvlink_total_nratom_data_transmitted	n/a
nvlink_total_ratom_data_transmitted	n/a
nvlink_total_response_data_received	n/a
nvlink_total_write_data_transmitted	n/a
nvlink_transmit_throughput	nvltx__bytes.sum.per_second
nvlink_user_data_received	nvlrx__bytes_data_user.sum
nvlink_user_data_transmitted	nvltx__bytes_data_user.sum
nvlink_user_nratom_data_transmitted	n/a
nvlink_user_ratom_data_transmitted	n/a
nvlink_user_response_data_received	n/a
nvlink_user_write_data_transmitted	n/a
pcie_total_data_received	pcie__read_bytes.sum
pcie_total_data_transmitted	pcie__write_bytes.sum
shared_efficiency	smsp__sass_average_data_bytes_per_wavefront_mem_shared.pct
shared_load_throughput	l1tex__data_pipe_lsu_wavefronts_mem_shared_op_ld.sum.per_second
shared_load_transactions	l1tex__data_pipe_lsu_wavefronts_mem_shared_op_ld.sum
shared_load_transactions_per_request	n/a
shared_store_throughput	l1tex__data_pipe_lsu_wavefronts_mem_shared_op_st.sum.per_second
shared_store_transactions	l1tex__data_pipe_lsu_wavefronts_mem_shared_op_st.sum
shared_store_transactions_per_request	n/a
shared_utilization	l1tex__data_pipe_lsu_wavefronts_mem_shared.avg.pct_of_peak_sustained_elapsed
single_precision_fu_utilization	smsp__pipe_fma_cycles_active.avg.pct_of_peak_sustained_active
sm_efficiency	smsp__cycles_active.avg.pct_of_peak_sustained_elapsed
sm_tex_utilization	l1tex__texin_sm2tex_req_cycles_active.avg.pct_of_peak_sustained_elapsed
special_fu_utilization	smsp__inst_executed_pipe_xu.avg.pct_of_peak_sustained_active
stall_constant_memory_dependency	smsp__warp_issue_stalled_imc_miss_per_warp_active.pct
stall_exec_dependency	smsp__warp_issue_stalled_short_scoreboard_per_warp_active.pct smsp__warp_issue_stalled_wait_per_warp_active.pct
stall_inst_fetch	smsp__warp_issue_stalled_no_instruction_per_warp_active.pct
stall_memory_dependency	smsp__warp_issue_stalled_long_scoreboard_per_warp_active.pct
stall_memory_throttle	smsp__warp_issue_stalled_drain_per_warp_active.pct smsp__warp_issue_stalled_lg_throttle_per_warp_active.pct
stall_not_selected	smsp__warp_issue_stalled_not_selected_per_warp_active.pct
stall_other	smsp__warp_issue_stalled_dispatch_stall_per_warp_active.pct smsp__warp_issue_stalled_misc_per_warp_active.pct
stall_pipe_busy	smsp__warp_issue_stalled_math_pipe_throttle_per_warp_active.pct smsp__warp_issue_stalled_mio_throttle_per_warp_active.pct
stall_sleeping	smsp__warp_issue_stalled_sleeping_per_warp_active.pct
stall_sync	smsp__warp_issue_stalled_barrier_per_warp_active.pct smsp__warp_issue_stalled_membar_per_warp_active.pct
stall_texture	smsp__warp_issue_stalled_tex_throttle_per_warp_active.pct
surface_atomic_requests	l1tex__t_requests_pipe_tex_mem_surface_op_atom.sum
surface_load_requests	l1tex__t_requests_pipe_tex_mem_surface_op_ld.sum
surface_reduction_requests	l1tex__t_requests_pipe_tex_mem_surface_op_red.sum
surface_store_requests	l1tex__t_requests_pipe_tex_mem_surface_op_st.sum
sysmem_read_bytes	lts__t_sectors_aperture_sysmem_op_read * 32
sysmem_read_throughput	lts__t_sectors_aperture_sysmem_op_read.sum.per_second
sysmem_read_transactions	lts__t_sectors_aperture_sysmem_op_read.sum
sysmem_read_utilization	n/a
sysmem_utilization	n/a
sysmem_write_bytes	lts__t_sectors_aperture_sysmem_op_write * 32
sysmem_write_throughput	lts__t_sectors_aperture_sysmem_op_write.sum.per_second
sysmem_write_transactions	lts__t_sectors_aperture_sysmem_op_write.sum
sysmem_write_utilization	n/a
tensor_precision_fu_utilization	sm__pipe_tensor_op_hmma_cycles_active.avg.pct_of_peak_sustained_active
tensor_precision_int_utilization	sm__pipe_tensor_op_imma_cycles_active.avg.pct_of_peak_sustained_active (SM 7.2 )
tex_cache_hit_rate	l1tex__t_sector_hit_rate.pct
tex_cache_throughput	n/a
tex_cache_transactions	l1tex__lsu_writeback_active.avg.pct_of_peak_sustained_active l1tex__tex_writeback_active.avg.pct_of_peak_sustained_active
tex_fu_utilization	smsp__inst_executed_pipe_tex.avg.pct_of_peak_sustained_active
tex_sm_tex_utilization	l1tex__f_tex2sm_cycles_active.avg.pct_of_peak_sustained_elapsed
tex_sm_utilization	sm__mio2rf_writeback_active.avg.pct_of_peak_sustained_elapsed
tex_utilization	n/a
texture_load_requests	l1tex__t_requests_pipe_tex_mem_texture.sum
warp_execution_efficiency	smsp__thread_inst_executed_per_inst_executed.ratio
warp_nonpred_execution_efficiency	smsp__thread_inst_executed_per_inst_executed.pct

2(1,2)

Sector reads from reductions are added here only for compatibility to the current definition of the metric in nvprof. Reductions do not cause data to be communicated from L2 back to L1.

4.6.4. Event Comparison

For nvprof events, the following table lists the equivalent metrics in NVIDIA Nsight Compute, if available. For a detailed explanation of the structuring of PerfWorks metrics, see Metrics Structure.

Metrics starting with sm__ are collected per-SM. Metrics starting with smsp__ are collected per-SM subpartition. However, all corresponding nvprof events are collected per-SM, only. Check the Metrics Guide for more details on these terms.

Table 12. Events Mapping Table from CUPTI Events to PerfWorks Metrics for Compute Capability >= 7.0

nvprof Event	PerfWorks Metric or Formula (>= SM 7.0)
active_cycles	sm__cycles_active.sum
active_cycles_pm	sm__cycles_active.sum
active_cycles_sys	sys__cycles_active.sum
active_warps	sm__warps_active.sum
active_warps_pm	sm__warps_active.sum
atom_count	smsp__inst_executed_op_generic_atom_dot_alu.sum
elapsed_cycles_pm	sm__cycles_elapsed.sum
elapsed_cycles_sm	sm__cycles_elapsed.sum
elapsed_cycles_sys	sys__cycles_elapsed.sum
fb_subp0_read_sectors	dram__sectors_read.sum
fb_subp1_read_sectors	dram__sectors_read.sum
fb_subp0_write_sectors	dram__sectors_write.sum
fb_subp1_write_sectors	dram__sectors_write.sum
global_atom_cas	smsp__inst_executed_op_generic_atom_dot_cas.sum
gred_count	smsp__inst_executed_op_global_red.sum
inst_executed	sm__inst_executed.sum
inst_executed_fma_pipe_s0	smsp__inst_executed_pipe_fma.sum
inst_executed_fma_pipe_s1	smsp__inst_executed_pipe_fma.sum
inst_executed_fma_pipe_s2	smsp__inst_executed_pipe_fma.sum
inst_executed_fma_pipe_s3	smsp__inst_executed_pipe_fma.sum
inst_executed_fp16_pipe_s0	smsp__inst_executed_pipe_fp16.sum
inst_executed_fp16_pipe_s1	smsp__inst_executed_pipe_fp16.sum
inst_executed_fp16_pipe_s2	smsp__inst_executed_pipe_fp16.sum
inst_executed_fp16_pipe_s3	smsp__inst_executed_pipe_fp16.sum
inst_executed_fp64_pipe_s0	smsp__inst_executed_pipe_fp64.sum
inst_executed_fp64_pipe_s1	smsp__inst_executed_pipe_fp64.sum
inst_executed_fp64_pipe_s2	smsp__inst_executed_pipe_fp64.sum
inst_executed_fp64_pipe_s3	smsp__inst_executed_pipe_fp64.sum
inst_issued1	sm__inst_issued.sum
l2_subp0_read_sector_misses	lts__t_sectors_op_read_lookup_miss.sum
l2_subp1_read_sector_misses	lts__t_sectors_op_read_lookup_miss.sum
l2_subp0_read_sysmem_sector_queries	lts__t_sectors_aperture_sysmem_op_read.sum
l2_subp1_read_sysmem_sector_queries	lts__t_sectors_aperture_sysmem_op_read.sum
l2_subp0_read_tex_hit_sectors	lts__t_sectors_srcunit_tex_op_read_lookup_hit.sum
l2_subp1_read_tex_hit_sectors	lts__t_sectors_srcunit_tex_op_read_lookup_hit.sum
l2_subp0_read_tex_sector_queries	lts__t_sectors_srcunit_tex_op_read.sum
l2_subp1_read_tex_sector_queries	lts__t_sectors_srcunit_tex_op_read.sum
l2_subp0_total_read_sector_queries	lts__t_sectors_op_read.sum lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum
l2_subp1_total_read_sector_queries	lts__t_sectors_op_read.sum lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum
l2_subp0_total_write_sector_queries	lts__t_sectors_op_write.sum lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum
l2_subp1_total_write_sector_queries	lts__t_sectors_op_write.sum lts__t_sectors_op_atom.sum lts__t_sectors_op_red.sum
l2_subp0_write_sector_misses	lts__t_sectors_op_write_lookup_miss.sum
l2_subp1_write_sector_misses	lts__t_sectors_op_write_lookup_miss.sum
l2_subp0_write_sysmem_sector_queries	lts__t_sectors_aperture_sysmem_op_write.sum
l2_subp1_write_sysmem_sector_queries	lts__t_sectors_aperture_sysmem_op_write.sum
l2_subp0_write_tex_hit_sectors	lts__t_sectors_srcunit_tex_op_write_lookup_hit.sum
l2_subp1_write_tex_hit_sectors	lts__t_sectors_srcunit_tex_op_write_lookup_hit.sum
l2_subp0_write_tex_sector_queries	lts__t_sectors_srcunit_tex_op_write.sum
l2_subp1_write_tex_sector_queries	lts__t_sectors_srcunit_tex_op_write.sum
not_predicated_off_thread_inst_executed	smsp__thread_inst_executed_pred_on.sum
pcie_rx_active_pulse	n/a
pcie_tx_active_pulse	n/a
prof_trigger_00	n/a
prof_trigger_01	n/a
prof_trigger_02	n/a
prof_trigger_03	n/a
prof_trigger_04	n/a
prof_trigger_05	n/a
prof_trigger_06	n/a
prof_trigger_07	n/a
inst_issued0	smsp__issue_inst0.sum
sm_cta_launched	sm__ctas_launched.sum
shared_load	smsp__inst_executed_op_shared_ld.sum
shared_store	smsp__inst_executed_op_shared_st.sum
generic_load	smsp__inst_executed_op_generic_ld.sum
generic_store	smsp__inst_executed_op_generic_st.sum
global_load	smsp__inst_executed_op_global_ld.sum
global_store	smsp__inst_executed_op_global_st.sum
local_load	smsp__inst_executed_op_local_ld.sum
local_store	smsp__inst_executed_op_local_st.sum
shared_atom	smsp__inst_executed_op_shared_atom.sum
shared_atom_cas	smsp__inst_executed_op_shared_atom_dot_cas.sum
shared_ld_bank_conflict	l1tex__data_bank_conflicts_pipe_lsu_mem_shared_op_ld.sum
shared_st_bank_conflict	l1tex__data_bank_conflicts_pipe_lsu_mem_shared_op_st.sum
shared_ld_transactions	l1tex__data_pipe_lsu_wavefronts_mem_shared_op_ld.sum
shared_st_transactions	l1tex__data_pipe_lsu_wavefronts_mem_shared_op_st.sum
tensor_pipe_active_cycles_s0	smsp__pipe_tensor_cycles_active.sum
tensor_pipe_active_cycles_s1	smsp__pipe_tensor_cycles_active.sum
tensor_pipe_active_cycles_s2	smsp__pipe_tensor_cycles_active.sum
tensor_pipe_active_cycles_s3	smsp__pipe_tensor_cycles_active.sum
thread_inst_executed	smsp__thread_inst_executed.sum
warps_launched	smsp__warps_launched.sum

4.6.5. Filtering

• Filtering by kernel name

  Both nvprof and NVIDIA Nsight Compute CLI support filtering which kernels’ data should be collected. In nvprof, the option is --kernels and applies to following metric collection options. In NVIDIA Nsight Compute CLI, the option is named --kernel-name and applies to the complete application execution. In other words, NVIDIA Nsight Compute CLI does not currently support collecting different metrics for different kernels, unless they execute on different GPU architectures.

• Filtering by kernel ID

  Nvprof allows users to specify which kernels to profile using a kernel ID description, using the same --kernels option. In NVIDIA Nsight Compute CLI, the syntax for this kernel ID is identical, but the option is named --kernel-id.

• Filtering by device

  Both nvprof and NVIDIA Nsight Compute CLI use --devices to filter the devices which to profile. In contrast to nvprof, in NVIDIA Nsight Compute CLI the option applies globally, not only to following options.

4.6.6. Output

• API trace and summary

  NVIDIA Nsight Compute CLI does not support any form of API-usage related output. No API data is captured during profiling.

• Dependency analysis

  NVIDIA Nsight Compute CLI does not support any dependency analysis. No API data is captured during profiling.

• GPU trace

  NVIDIA Nsight Compute CLI does not support any GPU trace output. Due to kernel replay during profiling, kernel executions are serialized, and start and end timestamps do not necessarily match those during application execution. In addition, no records for memory activities are recorded.

• Print summary

  While nvprof has several command line options to specify which summary information to print, NVIDIA Nsight Compute CLI uses further arguments to the --print-summary options. Profiling data can be summarized per-gpu, per-kernel or per-nvtx context.

• Kernel name demangling

  Nvprof allows users to decide between name demangling on or off using the --demangling options. NVIDIA Nsight Compute CLI currently always demangles kernel names in the output. In addition, the option --kernel-regex-base can be used to decide which name format should be used when matching kernel names during filtering.

• Pages

  Nvprof has no concept of output pages, all data is shown as a list or summarized. NVIDIA Nsight Compute CLI uses pages to define how data should be structured and printed. Those correspond to the report pages used in the GUI variant. The option --page can be used to select which page to show, and details is selected by default. All pages also support printing in CSV format for easier post-processing, using the --csv option.

4.6.7. Launch and Attach

• Launching a process for profiling

  In nvprof, the application to profile is passed to the tool as a command line argument. The application must be a local executable. Alternatively, you can choose to use the tool in a daemon mode and profile all applicable processes on the local machine (nvprof option --profile-all-processes). In nvprof, the decision to profile the complete process tree or only the root process is done via the --profile-child-processes flag. In NVIDIA Nsight Compute CLI, the --target-processes option is used for this.

  NVIDIA Nsight Compute CLI has several modes to determine which application to collect data for. By default, the executable passed via the command line to the tool is started, connected to, and profiled. This mode is called launch-and-attach.

• Launching a process for attach

  In contrast to nvprof, you can choose to only launch a local executable. In this mode (--mode launch), the process is started, connected to, but then suspended at the first CUDA API call. Subsequently, there is a third mode (--mode attach) to attach to any process launched using the aforementioned mode. In this case, all profiling and output options would be passed to the attaching instance of NVIDIA Nsight Compute CLI.

• Remote profiling

  Finally, using launch and attach, you can connect to a launched process on a remote machine, which could even run a different operating system than the local host. Use --hostname to select which remote host to connect to.

Notices

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