The Visual Profiler does not require any application changes; however, by making some simple modifications and additions, you can greatly increase its usability and effectiveness. Section Preparing An Application For Profiling describes how you can focus your profiling efforts and add extra annotations to your application that will greatly improve your profiling experience.

The first step in using the Visual Profiler to profile your application is to create a new profiling session. A session contains the settings, data, and results associated with your application. Sessions gives more information on working with sessions.

You can create a new session by selecting the Profile An Application link on the Welcome page, or by selecting New Session from the File menu. In the Create New Session dialog enter the executable for your application. Optionally, you can also specify the working directory, arguments, and environment.

If the Don't run guided analysis option was not selected when you created your session, the Visual Profiler will immediately run your application to collect the data needed for the first stage of guided analysis. As described in Analysis View, you can use the guided analysis system to get recommendations on performance limiting behavior in your application.

In addition to the guided analysis results, you will see a timeline for your application showing the CPU and GPU activity that occurred as your application executed. Read Timeline View and Properties View to learn how to explore the profiling information that is available in the timeline. Navigating the Timeline describes how you can zoom and scroll the timeline to focus on specific areas of your application.

In addition to the results provided in the Analysis View, you can also look at the specific metric and event values collected as part of the analysis. Metric and event values are displayed in the Details View. You can collect specific metric and event values that reveal how the kernels in your application are behaving. You collect metrics and events as described in the Details View section.

Some applications launch many tiny kernels, making them prone to very large (100s of megabytes or larger) nvprof output, even for application runs of only a few seconds. The Visual Profiler needs roughly the same amount of memory as the size of the profile it is opening/importing. The Java virtual machine may use a fraction of the main memory if no "max heap size" setting is specified. So depending on the size of main memory, Visual Profiler may fail to load some large files.

If the Visual Profiler fails to load a large profile, try setting the max heap size that JVM is allowed to use according to main memory size. You can modify the config file in libnvvp/nvvp.ini in the toolkit installation directory. The nvvp.ini configuration file looks like this:

-data
@user.home/nvvp_workspace
-vm
../jre/bin/java
-vmargs
-Dorg.eclipse.swt.browser.DefaultType=mozilla

To force the JVM to use 3 gigabytes of memory, for example, add a new line with -Xmx3G after -vmargs. The -Xmx setting should be tailored to the available system memory and input size. For example, if your system has 24GB of system memory, and you happen to know that you won't need to run any other memory-intensive applications at the same time as the Visual Profiler, so its okay for the profiler to take up the vast majority of that space. So you might pick, say, 22GB as the maximum heap size, leaving a few gigabytes for the OS, GUI, and any other programs that might be running.

Note: most CUDA installations require administrator/root-level access to modify this file.

The modified nvvp.ini file as per examples given above is as follows:

-data
@user.home/nvvp_workspace
-vm
../jre/bin/java
-d64
-vmargs
-Xms2g
-Xmx22g
-XX:+UseConcMarkSweepGC
-XX:+CMSIncrementalMode
-Dorg.eclipse.swt.browser.DefaultType=Mozilla

For more details on JVM settings, consult the Java virtual machine manual.

You can create a new executable session for your application by selecting the Profile An Application link on the Welcome page, or by selecting New Session from the File menu. Once a session is created, you can edit the session's settings as described in the Settings View.

You can open and save existing sessions using the open and save options in the File menu.

To analyze your application and to collect metric and event values, the Visual Profiler will execute your application multiple times. To get accurate profiling results, it is important that your application conform to the requirements detailed in Application Requirements.

You create an import session from the output of nvprof or the command-line profiler by using the Import... option in the File menu. Selecting this option opens the import wizard which guides you through the import process.

Because an executable application is not associated with an import session, the Visual Profiler cannot execute the application to collect additional profile data. As a result, analysis can only be performed with the data that is imported. Also, the Details View will show any imported event and metrics values but new metrics and events cannot be selected and collected for the import session.

The Timeline View shows CPU and GPU activity that occurred while your application was being profiled. Multiple timelines can be opened in the Visual Profiler at the same time. Each opened timeline is represented by a different instance of the view. The following figure shows a Timeline View for a CUDA application.

Along the top of the view is a horizontal ruler that shows elapsed time from the start of application profiling. Along the left of the view is a vertical ruler that describes what is being shown for each horizontal row of the timeline, and that contains various controls for the timeline. These controls are described in Timeline Controls

The timeline view is composed of timeline rows. Each row shows intervals that represent the start and end times of the activities that correspond to the type of the row. For example, timeline rows representing kernels have intervals representing the start and end times of executions of that kernel. In some cases (as noted below) a timeline row can display multiple sub-rows of activity. Sub-rows are used when there is overlapping activity. These sub-rows are created dynamically as necessary depending on how much activity overlap there is. The placement of intervals within certain sub-rows does not convey any particular meaning. Intervals are just packed into sub-rows using a heuristic that attempts to minimize the number of needed sub-rows. The height of the sub-rows is scaled to keep vertical space reasonable.

The types of timeline rows that are displayed in the Timeline View are:

Process

A timeline will contain a Process row for each application profiled. The process identifier represents the pid of the process. The timeline row for a process does not contain any intervals of activity. Threads within the process are shown as children of the process.

Thread

A timeline will contain a Thread row for each CPU thread in the profiled application that performed either a CUDA driver or CUDA runtime API call. The thread identifier is a unique id for that CPU thread. The timeline row for a thread is does not contain any intervals of activity.

Runtime API

A timeline will contain a Runtime API row for each CPU thread that performs a CUDA Runtime API call. Each interval in the row represents the duration of the call on the corresponding thread.

Driver API

A timeline will contain a Driver API row for each CPU thread that performs a CUDA Driver API call. Each interval in the row represents the duration of the call on the corresponding thread.

Markers and Ranges

A timeline will contain a single Markers and Ranges row for each CPU thread that uses the NVIDIA Tools Extension API to annotate a time range or marker. Each interval in the row represents the duration of a time range, or the instantaneous point of a marker. This row will have sub-rows if there are overlapping ranges.

Profiling Overhead

A timeline will contain a single Profiling Overhead row for each process. Each interval in the row represents the duration of execution of some activity required for profiling. These intervals represent activity that does not occur when the application is not being profiled.

Device

A timeline will contain a Device row for each GPU device utilized by the application being profiled. The name of the timeline row indicates the device ID in square brackets followed by the name of the device. After running the Compute Utilization analysis, the row will contain an estimate of the compute utilization of the device over time. If power, clock, and thermal profiling are enabled, the row will also contain points representing those readings.

Context

A timeline will contains a Context row for each CUDA context on a GPU device. The name of the timeline row indicates the context ID or the custom context name if the NVIDIA Tools Extension API was used to name the context. The row for a context does not contain any intervals of activity.

Memcpy

A timeline will contain memory copy row(s) for each context that performs memcpys. A context may contain up to four memcpy rows for device-to-host, host-to-device, device-to-device, and peer-to-peer memory copies. Each interval in a row represents the duration of a memcpy executing on the GPU.

Compute

A timeline will contain a Compute row for each context that performs computation on the GPU. Each interval in a row represents the duration of a kernel on the GPU device. The Compute row indicates all the compute activity for the context. Sub-rows are used when concurrent kernels are executed on the context. All kernel activity, including kernels launched using CUDA Dynamic Parallelism, is shown on the Compute row. The Kernel rows following the Compute row show activity of each individual application kernel.

Kernel

A timeline will contain a Kernel row for each kernel executed by the application. Each interval in a row represents the duration of execution of an instance of that kernel in the containing context. Each row is labeled with a percentage that indicates the total execution time of all instances of that kernel compared to the total execution time of all kernels. For each context, the kernels are ordered top to bottom by this execution time percentage. Sub-rows are used to show concurrent kernel execution. For CUDA Dynamic Parallelism applications, the kernels are organized in a hierarchy that represents the parent/child relationship between the kernels. Host-launched kernels are shown as direct children of the Context row. Kernels that use CUDA Dynamic Parallelism to launch other kernels can be expanded using the '+' icon to show the kernel rows representing those child kernels. For kernels that don't launch child kernels, the kernel execution is represented by a solid interval, showing the time that that instance of the kernel was executing on the GPU. For kernels that launch child kernels, the interval can also include a hollow part at the end. The hollow part represents the time after the kernel has finished executing where it is waiting for child kernels to finish executing. The CUDA Dynamic Parallelism execution model requires that a parent kernel not complete until all child kernels complete and this is what the hollow part is showing. The Focus control described in Timeline Controls can be used to control display of the parent/child timelines.

Stream

A timeline will contain a Stream row for each stream used by the application (including both the default stream and any application created streams). Each interval in a Stream row represents the duration of a memcpy or kernel execution performed on that stream.

The Analysis View is used to control application analysis and to display the analysis results. There are two analysis modes: guided and unguided. In guided mode the analysis system will guide you though multiple analysis stages to help you understand the likely performance limiters and optimization opportunities in your application. In unguided mode you can manually explore all the analysis results collect for you application. The following figure shows the analysis view in guided analysis mode. The left part of the view provides step-by-step directions to help you analyse and optimize your application. The right part of the view shows you detailed analysis results appropriate for each part of the analysis.

Unguided Application Analysis

In unguided analysis mode each application analysis stage has a Run analysis button that can be used to generate the analysis results for that stage. When the Run analysis button is selected, the Visual Profiler will execute the application to collect the profiling data needed to perform the analysis. The green check-mark next to an analysis stage indicates that the analysis results for that stage are available. Each analysis result contains a brief description of the analysis and a More… link to detailed documentation on the analysis. When you select an analysis result, the timeline rows or intervals associated with that result are highlighted in the Timeline View.

When a single kernel instance is selected in the timeline, additional kernel-specific analysis stages are available. Each kernel-specific analysis stage has a Run analysis button that operates in the same manner as for the application analysis stages. The following figure shows the analysis results for the Divergent Execution analysis stage. Some kernel instance analysis results, like Divergent Execution are associated with specific source-lines within the kernel. To see the source associated with each result, select an entry from the table. The source-file associated with that entry will open.

The Details View displays a table of information for each memory copy and kernel execution in the profiled application. The following figure shows the table containing several memcpy and kernel executions. Each row of the table contains general information for a kernel execution or memory copy. For kernels, the table will also contain a column for each metric or event value collected for that kernel. In the figure, the Achieved Occupancy column shows the value of that metric for each of the kernel executions.

You can sort the data by a column by left clicking on the column header, and you can rearrange the columns by left clicking on a column header and dragging it to its new location. If you select a row in the table, the corresponding interval will be selected in the Timeline View. Similarly, if you select a kernel or memcpy interval in the Timeline View the table will be scrolled to show the corresponding data.

If you hover the mouse over a column header, a tooltip will display describing the data shown in that column. For a column containing event or metric data, the tooltip will describe the corresponding event or metric. Section Metrics Reference contains more detailed information about each metric.

The Properties View shows information about the row or interval highlighted or selected in the Timeline View. If a row or interval is not selected, the displayed information tracks the motion of the mouse pointer. If a row or interval is selected, the displayed information is pinned to that row or interval.

The Console View shows the stdout and stderr output of the application each time it executes. If you need to provide stdin input to you application, you do so by typing into the console view.

The Settings View allows you to specify execution settings for the application being profiled. As shown in the following figure, the Executable settings tab allows you to specify the executable file for the application, the working directory for the application, the command-line arguments for the application, and the environment for the application. Only the executable file is required, all other fields are optional.

To resize a view, simply left click and drag on the dividing area between the views. All views stacked together in one area are resized at the same time.

To reorder a view in a stacked set of views, left click and drag the view tab to the new location within the view stack.

To move a view, left click the view tab and drag it to its new location. As you drag the view, an outline will show the target location for the view. You can place the view in a new location, or stack it in the same location as other views.

You can undock a view from the Visual Profiler window so that the view occupies its own stand-alone window. You may want to do this to take advantage of multiple monitors or to maximum the size of an individual view. To undock a view, left click the view tab and drag it outside of the Visual Profiler window. To dock a view, left click the view tab (not the window decoration) and drag it into the Visual Profiler window.

Use the X icon on a view tab to close a view. To open a view, use the View menu.

GPU-Trace and API-Trace modes can be enabled individually or at the same time. GPU-trace mode provides a timeline of all activities taking place on the GPU in chronological order. Each kernel execution and memory copy/set instance is shown in the output. For each kernel or memory copy detailed information such as kernel parameters, shared memory usage and memory transfer throughput are shown. The number shown in the square brackets after the kernel name correlates to the CUDA API that launched that kernel.

Here's an example:

$ nvprof --print-gpu-trace matrixMul
==27706== NVPROF is profiling process 27706, command: matrixMul
==27706== Profiling application: matrixMul
[Matrix Multiply Using CUDA] - Starting...
GPU Device 0: "GeForce GT 640M LE" with compute capability 3.0

MatrixA(320,320), MatrixB(640,320)
Computing result using CUDA Kernel...
done
Performance= 35.36 GFlop/s, Time= 3.707 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block
Checking computed result for correctness: OK

Note: For peak performance, please refer to the matrixMulCUBLAS example.
==27706== Profiling result:
   Start  Duration            Grid Size      Block Size     Regs*    SSMem*    DSMem*      Size  Throughput           Device   Context    Stream  Name
133.81ms  135.78us                    -               -         -         -         -  409.60KB  3.0167GB/s  GeForce GT 640M         1         2  [CUDA memcpy HtoD]
134.62ms  270.66us                    -               -         -         -         -  819.20KB  3.0267GB/s  GeForce GT 640M         1         2  [CUDA memcpy HtoD]
134.90ms  3.7037ms            (20 10 1)       (32 32 1)        29  8.1920KB        0B         -           -  GeForce GT 640M         1         2  void matrixMulCUDA<int=32>(float*, float*, float*, int, int) [94]
138.71ms  3.7011ms            (20 10 1)       (32 32 1)        29  8.1920KB        0B         -           -  GeForce GT 640M         1         2  void matrixMulCUDA<int=32>(float*, float*, float*, int, int) [105]
<...more output...>
1.24341s  3.7011ms            (20 10 1)       (32 32 1)        29  8.1920KB        0B         -           -  GeForce GT 640M         1         2  void matrixMulCUDA<int=32>(float*, float*, float*, int, int) [2191]
1.24711s  3.7046ms            (20 10 1)       (32 32 1)        29  8.1920KB        0B         -           -  GeForce GT 640M         1         2  void matrixMulCUDA<int=32>(float*, float*, float*, int, int) [2198]
1.25089s  248.13us                    -               -         -         -         -  819.20KB  3.3015GB/s  GeForce GT 640M         1         2  [CUDA memcpy DtoH]

Regs: Number of registers used per CUDA thread. This number includes registers used internally by the CUDA driver and/or tools and can be more than what the compiler shows.
SSMem: Static shared memory allocated per CUDA block.
DSMem: Dynamic shared memory allocated per CUDA block.

nvprof supports CUDA Dynamic Parallelism in GPU-trace mode. For host kernel launch, the kernel ID will be shown. For device kernel launch, the kernel ID, parent kernel ID and parent block will be shown. Here's an example:

$nvprof --print-gpu-trace cdpSimpleQuicksort
==28128== NVPROF is profiling process 28128, command: cdpSimpleQuicksort
Running on GPU 0 (Tesla K20c)
Initializing data:
Running quicksort on 128 elements
Launching kernel on the GPU
Validating results: OK
==28128== Profiling application: cdpSimpleQuicksort
==28128== Profiling result:
   Start  Duration            Grid Size      Block Size     Regs*    SSMem*    DSMem*      Size  Throughput           Device   Context    Stream          ID   Parent ID         Parent Block  Name
192.76ms  1.2800us                    -               -         -         -         -      512B  400.00MB/s   Tesla K20c (0)         1         2           -           -                    -  [CUDA memcpy HtoD]
193.31ms  146.02us              (1 1 1)         (1 1 1)        32        0B        0B         -           -   Tesla K20c (0)         1         2           2           -                    -  cdp_simple_quicksort(unsigned int*, int, int, int) [171]
193.41ms  110.53us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2          -5           2              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.45ms  125.57us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2          -6           2              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.48ms  9.2480us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2          -7          -5              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.52ms  107.23us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2          -8          -5              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.53ms  93.824us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2          -9          -6              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.57ms  117.47us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -10          -6              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.58ms  5.0560us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -11          -8              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.62ms  108.06us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -12          -8              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.65ms  113.34us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -13         -10              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.68ms  29.536us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -14         -12              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.69ms  22.848us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -15         -10              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.71ms  130.85us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -16         -13              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.73ms  62.432us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -17         -12              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.76ms  41.024us              (1 1 1)         (1 1 1)        32        0B      256B         -           -   Tesla K20c (0)         1         2         -18         -13              (0 0 0)  cdp_simple_quicksort(unsigned int*, int, int, int)
193.92ms  2.1760us                    -               -         -         -         -      512B  235.29MB/s   Tesla K20c (0)         1         2           -           -                    -  [CUDA memcpy DtoH]

Regs: Number of registers used per CUDA thread. This number includes registers used internally by the CUDA driver and/or tools and can be more than what the compiler shows.
SSMem: Static shared memory allocated per CUDA block.
DSMem: Dynamic shared memory allocated per CUDA block.

API-trace mode shows the timeline of all CUDA runtime and driver API calls invoked on the host in chronological order. Here's an example:

$nvprof --print-api-trace matrixMul
==27722== NVPROF is profiling process 27722, command: matrixMul
==27722== Profiling application: matrixMul
[Matrix Multiply Using CUDA] - Starting...
GPU Device 0: "GeForce GT 640M LE" with compute capability 3.0

MatrixA(320,320), MatrixB(640,320)
Computing result using CUDA Kernel...
done
Performance= 35.35 GFlop/s, Time= 3.708 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block
Checking computed result for correctness: OK

Note: For peak performance, please refer to the matrixMulCUBLAS example.
==27722== Profiling result:
   Start  Duration  Name
108.38ms  6.2130us  cuDeviceGetCount
108.42ms     840ns  cuDeviceGet
108.42ms  22.459us  cuDeviceGetName
108.45ms  11.782us  cuDeviceTotalMem
108.46ms     945ns  cuDeviceGetAttribute
149.37ms  23.737us  cudaLaunch (void matrixMulCUDA<int=32>(float*, float*, float*, int, int) [2198])
149.39ms  6.6290us  cudaEventRecord
149.40ms  1.10156s  cudaEventSynchronize
<...more output...>
1.25096s  21.543us  cudaEventElapsedTime
1.25103s  1.5462ms  cudaMemcpy
1.25467s  153.93us  cudaFree
1.25483s  75.373us  cudaFree
1.25491s  75.564us  cudaFree
1.25693s  10.901ms  cudaDeviceReset

To see a list of all available events on a particular NVIDIA GPU, type nvprof --query-events. To see a list of all available metrics on a particular NVIDIA GPU, type nvprof --query-metrics. nvprof is able to collect multiple events/metrics at the same time. Here's an example:

$ nvprof --events warps_launched,branch --metrics ipc matrixMul
[Matrix Multiply Using CUDA] - Starting...
==60544== NVPROF is profiling process 60544, command: matrixMul
GPU Device 0: "GeForce GT 640M LE" with compute capability 3.0

MatrixA(320,320), MatrixB(640,320)
Computing result using CUDA Kernel...
==60544== Some kernel(s) will be replayed on device 0 in order to collect all events/metrics.
done
Performance= 7.75 GFlop/s, Time= 16.910 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block
Checking computed result for correctness: OK

Note: For peak performance, please refer to the matrixMulCUBLAS example.
==60544== Profiling application: matrixMul
==60544== Profiling result:
==60544== Event result:
Invocations                      Event Name         Min         Max         Avg
Device "GeForce GT 640M LE (0)"
        Kernel: void matrixMulCUDA<int=32>(float*, float*, float*, int, int)
        301                  warps_launched        6400        6400        6400
        301                          branch       70400       70400       70400

==60544== Metric result:
Invocations                     Metric Name              Metric Description         Min         Max         Avg
Device "GeForce GT 640M LE (0)"
        Kernel: void matrixMulCUDA<int=32>(float*, float*, float*, int, int)
        301                             ipc                    Executed IPC    1.386412    1.393312    1.390278

If the specified events/metrics can't be profiled in a single run of the application, nvprof by default replays each kernel multiple times until all the events/metrics are collected.

Option --replay-mode <mode> can be used to change the replay mode. In "application replay" mode, nvprof re-runs the whole application instead of replaying each kernel, in order to collect all events/metrics. This in some cases can be faster than kernel replay mode if the application allocates large amount of device memory. Replay can also be turned off, in which case the profiler will fail to collect some events/metrics.

To collect all events available on each device, use option --events all.

To collect all metrics available on each device, use option --metrics all.

In event/metric trace mode, event and metric values are shown for each kernel execution. By default, event and metric values are aggregated across all units in the GPU. For example, by default multiprocessor specific events are aggregated across all multiprocessors on the GPU. If --aggregate-mode off is specified, values of each unit are shown. For example, in the following example, the "branch" event value is shown for each multiprocessor on the GPU.

$ nvprof --aggregate-mode off --events branch --print-gpu-trace matrixMul
[Matrix Multiply Using CUDA] - Starting...
==60642== NVPROF is profiling process 60642, command: matrixMul
GPU Device 0: "GeForce GT 640M LE" with compute capability 3.0

MatrixA(320,320), MatrixB(640,320)
Computing result using CUDA Kernel...
done
Performance= 23.73 GFlop/s, Time= 5.523 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block
Checking computed result for correctness: OK

Note: For peak performance, please refer to the matrixMulCUBLAS example.
==60642== Profiling application: matrixMul
==60642== Profiling result:
         Device          Context           Stream                Kernel  branch (0)  branch (1)
GeForce GT 640M                1                2  void matrixMulCUDA<i       35200       35200
GeForce GT 640M                1                2  void matrixMulCUDA<i       35200       35200
<...more output...>

A timeout (in seconds) can be provided to nvprof. The CUDA application being profiled will be killed by nvprof after the timeout. Profiling result collected before the timeout will be shown.

Concurrent-kernel profiling is supported, and is turned on by default. To turn the feature off, use the option --concurrent-kernels off. This forces concurrent kernel executions to be serialized when a CUDA application is run with nvprof.

When collecting events/metrics, nvprof profiles all kernels launched on all visible CUDA devices by default. This profiling scope can be limited by the following options.

--devices <device IDs> applies to --events, --metrics, --query-events and --query-metrics options that follows it. It limits these options to collect events/metrics only on the devices specified by <device IDs>, which can be a list of device ID numbers separated by comma.

--kernels <kernel filter> applies to --events and --metrics options that follows it. It limits these options to collect events/metrics only on the kernels specified by <kernel filter>, which has the following syntax:

<kernel name>

or

<context id/name>:<stream id/name>:<kernel name>:<invocation>

Each string in the angle brackets can be a standard Perl regular expression. Empty string matches any number or character combination.

Invocation number n indicates the nth invocation of the kernel. If invocation is a positive number, it's strictly matched against the invocation of the kernel. Otherwise it's treated as a regular expression.

If the context/stream string is a positive number, it's strictly matched against the cuda context/stream ID. Otherwise it's treated as a regular expression and matched against the context/stream name provided by the NVIDIA Tools Extension.

Both --devices and --kernels can be specified multiple times, with distinct events/metrics associated.

--events, --metrics, --query-events and --query-metrics are controlled by the nearest scope options before them.

As an example, the following command,

nvprof --devices 0 --metrics ipc --kernels "1:foo:bar:2" --events local_load a.out

collects metric ipc on all kernels launched on device 0. It also collects event local_load for any kernel whose name contains bar and is the 2nd instance launched on context 1 and on stream named foo on device 0.

By default, nvprof only profiles the application specified by the command-line argument. It doesn't trace child processes launched by that process. To profile all processes launched by an application, use the --profile-child-process option.

nvprof also has a "profile all processes" mode, in which it profiles every CUDA process launched on the same system by the same user who launched nvprof. Exit this mode by typing "Ctrl-c".

For devices that support system profiling, nvprof can enable low frequency sampling of the power, clock, and thermal behavior of each GPU used by the application. This feature is turned off by default. To turn on this feature, use --system-profiling on. To see the detail of each sample point, combine the above option with --print-gpu-trace.

For devices that support Unified Memory, nvprof collects the Unified Memory related memory traffic to and from each GPU on your system. This feature is turned off by default. To turn on this feature, use --unified-memory-profiling <per-process-device|off>. To see the detail of each memory transfer, combine the above option with --print-gpu-trace.

On multi-GPU configurations without P2P support between any pair of devices that support Unified Memory, managed memory allocations are placed in zero-copy memory. In this case Unified Memory profiling is not supported. In certain cases, the environment variable CUDA_MANAGED_FORCE_DEVICE_ALLOC can be set to force managed allocations to be in device memory and to enable migration on these hardware configurations. In this case Unified Memory profiling is supported. Normally, using the environment variable CUDA_VISIBLE_DEVICES is recommended to restrict CUDA to only use those GPUs that have P2P support. Please refer to the environment variables section in the CUDA C Programming Guide for further details.

By default, nvprof adjusts the time units automatically to get the most precise time values. The --normalized-time-unit options can be used to get fixed time units throughout the results.

For each profiling mode, option --csv can be used to generate output in comma-separated values (CSV) format. The result can be directly imported to spreadsheet software such as Excel.

For each profiling mode, option --output-profile can be used to generate a result file. This file is not human-readable, but can be imported to nvprof using the option --import-profile, or into the Visual Profiler.

By default, nvprof demangles C++ function names. Use option --demangling off to turn this feature off.

By default, nvprof sends most of its output to stderr. To redirect the output, use --log-file. --log-file %1 tells nvprof to redirect all output to stdout. --log-file <filename> redirects output to a file. Use %p in the filename to be replaced by the process ID of nvprof, %h by the hostname , %q{ENV} by the value of environment variable ENV, and %% by %.

Table 2 contains the options supported by the command line profiler. Note the following regarding the profiler log that is produced from the different options:

• Typically, each profiler option corresponds to a single column is output. There are a few exceptions in which case multiple columns are output; these are noted where applicable in Table 2.
• In most cases the column name is the same as the option name; the exceptions are listed in Table 2.
• In most cases the column values are 32-bit integers in decimal format; the exceptions are listed in Table 2.

counterA 
cudadevice 0
counterB
cudadevice 1
counterC

The command line profiler supports logging of event counters during kernel execution. The list of available events can be found using nvprof --query-events as described in Event/metric Summary Mode. The event name can be used in the command line profiler configuration file. In every application run only a few counter values can be collected. The number of counters depends on the specific counters selected.

There are three common remote profiling use cases that can be addressed by using nvprof and nvvp.

$ nvprof --output-profile timeline.nvprof <app> <app args>
      

$ nvprof --metrics achieved_occupancy,executed_ipc -o metrics.nvprof <app> <app args> 
      

$ nvprof --kernels <kernel specifier> --analysis-metrics -o analysis.nvprof <app> <app args> 
      

The collected profile data is viewed and analyzed by importing it into nvvp on the host system. See Import Session for more information about importing.

A marker is used to describe an instantaneous event. A marker can contain a text message or specify additional information using the event attributes structure. Use nvtxMarkA to create a marker containing an ASCII message. Use nvtxMarkEx() to create a marker containing additional attributes specified by the event attribute structure. The nvtxMarkW() function is not supported in the CUDA implementation of NVTX and has no effect if called.

    nvtxMarkA("My mark");

    nvtxEventAttributes_t eventAttrib = {0};
    eventAttrib.version = NVTX_VERSION;
    eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
    eventAttrib.colorType = NVTX_COLOR_ARGB;
    eventAttrib.color = COLOR_RED;
    eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
    eventAttrib.message.ascii = "my mark with attributes";
    nvtxMarkEx(&eventAttrib);
      

A start/end range is used to denote an arbitrary, potentially non-nested, time span. The start of a range can occur on a different thread than the end of the range. A range can contain a text message or specify additional information using the event attributes structure. Use nvtxRangeStartA() to create a marker containing an ASCII message. Use nvtxRangeStartEx() to create a range containing additional attributes specified by the event attribute structure. The nvtxRangeStartW() function is not supported in the CUDA implementation of NVTX and has no effect if called. For the correlation of a start/end pair, a unique correlation ID is created that is returned from nvtxRangeStartA() or nvtxRangeStartEx(), and is then passed into nvtxRangeEnd().

    // non-overlapping range
    nvtxRangeId_t id1 = nvtxRangeStartA("My range");
    nvtxRangeEnd(id1);

    nvtxEventAttributes_t eventAttrib = {0};
    eventAttrib.version = NVTX_VERSION;
    eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
    eventAttrib.colorType = NVTX_COLOR_ARGB;
    eventAttrib.color = COLOR_BLUE;
    eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
    eventAttrib.message.ascii = "my start/stop range";
    nvtxRangeId_t id2 = nvtxRangeStartEx(&eventAttrib);
    nvtxRangeEnd(id2);

    // overlapping ranges
    nvtxRangeId_t r1 = nvtxRangeStartA("My range 0");
    nvtxRangeId_t r2 = nvtxRangeStartA("My range 1");
    nvtxRangeEnd(r1);
    nvtxRangeEnd(r2);
      

A push/pop range is used to denote nested time span. The start of a range must occur on the same thread as the end of the range. A range can contain a text message or specify additional information using the event attributes structure. Use nvtxRangePushA() to create a marker containing an ASCII message. Use nvtxRangePushEx() to create a range containing additional attributes specified by the event attribute structure. The nvtxRangePushW() function is not supported in the CUDA implementation of NVTX and has no effect if called. Each push function returns the zero-based depth of the range being started. The nvtxRangePop() function is used to end the most recently pushed range for the thread. nvtxRangePop() returns the zero-based depth of the range being ended. If the pop does not have a matching push, a negative value is returned to indicate an error.

    nvtxRangePushA("outer");
    nvtxRangePushA("inner");
    nvtxRangePop();  // end "inner" range
    nvtxRangePop();  // end "outer" range

    nvtxEventAttributes_t eventAttrib = {0};
    eventAttrib.version = NVTX_VERSION;
    eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
    eventAttrib.colorType = NVTX_COLOR_ARGB;
    eventAttrib.color = COLOR_GREEN;
    eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
    eventAttrib.message.ascii = "my push/pop range";
    nvtxRangePushEx(&eventAttrib);
    nvtxRangePop();
      

The events attributes structure, nvtxEventAttributes_t, is used to describe the attributes of an event. The layout of the structure is defined by a specific version of NVTX and can change between different versions of the Tools Extension library.

    nvtxEventAttributes_t eventAttrib = {0};
    eventAttrib.version = NVTX_VERSION;
    eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
    eventAttrib.colorType = NVTX_COLOR_ARGB;
    eventAttrib.color = ::COLOR_YELLOW;
    eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
    eventAttrib.message.ascii = "My event";
    nvtxMarkEx(&eventAttrib);