Gopesh Bhardwaj
20 KiB
rocprofv3 user guide
ROCProfiler SDK is a tooling infrastructure designed for profiling general-purpose GPU compute applications running on the ROCm platform. It supports application tracing to provide a big picture of the GPU application execution and kernel profiling to provide low-level hardware details from the performance counters.
The ROCProfiler SDK library provides runtime-independent APIs for tracing runtime calls and asynchronous activities such as GPU kernel dispatches and memory moves. The tracing includes callback APIs for runtime API tracing and activity APIs for asynchronous activity records logging. You can utilize these APIs to develop a tracing tool or to implement tracing in the application.
In this document, we discuss the command-line tool rocprofv3 in detail, which is based on the APIs from the ROCProfiler SDK library.
Installation
To install ROCProfiler SDK from source, follow the instructions provided in the sections below:
Prerequisites
-
Linux operating system. Here is the list of operating systems tested for ROCProfiler SDK support:
- Ubuntu 20.04
- Ubuntu 22.04
- OpenSUSE 15.4
- RedHat 8.8
To check a system’s operating system and version, see the
/etc/os-releaseand/usr/lib/os-releasefiles:$ cat /etc/os-release NAME="Ubuntu" VERSION="20.04.4 LTS (Focal Fossa)" ID=ubuntu ... VERSION_ID="20.04" ... -
Cmake version 3.21 or higher.
-
To install a new version of Cmake, we recommend using PyPi (Python’s pip):
pip install --user 'cmake==3.21.0' export PATH=${HOME}/.local/bin:${PATH}
Build
To build ROCProfiler SDK, use:
git clone https://git@github.com:ROCm/rocprofiler-sdk-internal.git rocprofiler-sdk-source
cmake --build rocprofiler-sdk-build --target all --parallel 8
To see the various build options along with their default values, use:
$ cmake -LA
REPRODUCIBLE_RUNTIME_USE_MPI:BOOL=OFF
ROCPROFILER_BLACK_FORMAT_EXE:FILEPATH=ROCPROFILER_BLACK_FORMAT_EXE-NOTFOUND
ROCPROFILER_BUILD_CI:BOOL=OFF
ROCPROFILER_BUILD_CODECOV:BOOL=OFF
ROCPROFILER_BUILD_DEBUG:BOOL=OFF
ROCPROFILER_BUILD_DEVELOPER:BOOL=OFF
ROCPROFILER_BUILD_DOCS:BOOL=OFF
ROCPROFILER_BUILD_FMT:BOOL=ON
ROCPROFILER_BUILD_GHC_FS:BOOL=ON
ROCPROFILER_BUILD_GLOG:BOOL=ON
ROCPROFILER_BUILD_GTEST:BOOL=ON
ROCPROFILER_BUILD_RELEASE:BOOL=OFF
ROCPROFILER_BUILD_SAMPLES:BOOL=OFF
ROCPROFILER_BUILD_STACK_PROTECTOR:BOOL=ON
ROCPROFILER_BUILD_STATIC_LIBGCC:BOOL=OFF
ROCPROFILER_BUILD_STATIC_LIBSTDCXX:BOOL=OFF
ROCPROFILER_BUILD_TESTS:BOOL=ON
ROCPROFILER_BUILD_WERROR:BOOL=OFF
ROCPROFILER_CLANG_FORMAT_EXE:FILEPATH=ROCPROFILER_CLANG_FORMAT_EXE-NOTFOUND
ROCPROFILER_CLANG_TIDY_COMMAND:FILEPATH=ROCPROFILER_CLANG_TIDY_COMMAND-NOTFOUND
ROCPROFILER_CMAKE_FORMAT_EXE:FILEPATH=ROCPROFILER_CMAKE_FORMAT_EXE-NOTFOUND
ROCPROFILER_CPACK_SYSTEM_NAME:STRING=Linux
ROCPROFILER_DEBUG_TRACE:BOOL=OFF
ROCPROFILER_DEFAULT_ROCM_PATH:PATH=/opt/rocm-6.1.0-13278
ROCPROFILER_ENABLE_CLANG_TIDY:BOOL=OFF
ROCPROFILER_LD_AQLPROFILE:BOOL=OFF
ROCPROFILER_MEMCHECK:STRING=
ROCPROFILER_REGENERATE_COUNTERS_PARSER:BOOL=OFF
ROCPROFILER_UNSAFE_NO_VERSION_CHECK:BOOL=OFF
Install
To install ROCProfiler SDK from the rocprofiler-sdk-build directory, run:
cmake --build rocprofiler-sdk-build --target install
Test
To run the build tests, cd into the rocprofiler-sdk-build directory and run:
ctest -R
Usage
rocprofv3 is a CLI tool that helps you to quickly optimize the applications and understand the low-level kernel details without requiring any modification in the source code. rocprofv3 is being developed to be backward compatible with its predecessor rocprof along with providing more features to help users profile their applications with better accuracy.
The usage of rocprofv3 for application tracing and kernel profiling using various command-line options is demonstrated in the following sections.
rocprofv3 is installed with ROCm under /opt/rocm/bin. To use the tool from anywhere in the system, export PATH variable:
export PATH=$PATH:/opt/rocm/bin
Before you can start tracing or profiling your HIP application using rocprofv3, build the application using:
cmake -B <build-directory> <source-directory> -DCMAKE_PREFIX_PATH=/opt/rocm
cmake --build <build-directory> --target all --parallel <N>
Options
Below is the list of rocprofv3 command-line options. Some options are used for application tracing and some for kernel profiling while the output control options control the presentation and redirection of the generated output.
| Option | Description | Use |
|---|---|---|
| -d | --output-directory | Specifies the path for the output files. | Output control |
| --hip-trace | Collects HIP runtime traces. | Application tracing |
| --hip-runtime-trace | Collects HIP runtime API traces. | Application tracing |
| --hip-compiler-trace | Collects HIP compiler generated code traces. | Application tracing |
| --scratch-memory-trace | Collects scratch memory operations traces. | Application tracing |
| --hsa-trace | Collects HSA API traces. | Application tracing |
| --hsa-core-trace | Collects HSA API traces (core API). | Application tracing |
| --hsa-amd-trace | Collects HSA API traces (AMD-extension API). | Application tracing |
| --hsa-image-trace | Collects HSA API Ttaces (Image-extenson API). | Application tracing |
| --hsa-finalizer-trace | Collects HSA API traces (Finalizer-extension API). | Application tracing |
| -i | Specifies the input file. | Kernel profiling |
| -L | --list-metrics | List metrics for counter collection. | Kernel profiling |
| --kernel-trace | Collects kernel dispatch traces. | Application tracing |
| -M | --mangled-kernels | Overrides the default demangling of kernel names. | Output control |
| --marker-trace | Collects marker (ROC-TX) traces. | Application tracing |
| --memory-copy-trace | Collects memory copy traces. | Application tracing |
| -o | --output-file | Specifies the name of the output file. Note that this name is appended to the default names (_api_trace or counter_collection.csv) of the generated files. | Output control |
| --sys-trace | Collects HIP, HSA, memory copy, marker, and kernel dispatch traces. | Application Tracing |
| -T | --truncate-kernels | Truncates the demangled kernel names for improved readability. | Output control |
You can also see all the rocprofv3 options using:
rocprofv3 --help
Application tracing
Application tracing provides the big picture of a program’s execution by collecting data on the execution times of API calls and GPU commands, such as kernel execution, async memory copy, and barrier packets. This information can be used as the first step in the profiling process to answer important questions, such as how much percentage of time was spent on memory copy and which kernel took the longest time to execute.
To use rocprofv3 for application tracing, run:
rocprofv3 <tracing_option> <app_relative_path>
HIP trace
HIP trace comprises of execution traces for the entire application at the HIP level. This includes HIP API functions and their asynchronous activities at the runtime level. In general, HIP APIs directly interact with the user program. It is easier to analyze HIP traces as you can directly map the traces to the program.
To trace HIP runtime APIs, use:
rocprofv3 --hip-trace < app_relative_path >
Running the above command generates a hip_api_trace.csv file prefixed with the process Id.
$ cat 238_hip_api_trace.csv
"Domain","Function","Process_Id","Thread_Id","Correlation_Id","Start_Timestamp","End_Timestamp"
"HIP_RUNTIME_API","hipGetDevicePropertiesR0600",238,238,1,1191915574691984,1191915687784011
"HIP_RUNTIME_API","hipMalloc",238,238,2,1191915691312459,1191915691388696
"HIP_RUNTIME_API","hipMalloc",238,238,3,1191915691390637,1191915691423279
"HIP_RUNTIME_API","hipMemcpy",238,238,4,1191915691439107,1191916547828448
"HIP_RUNTIME_API","hipLaunchKernel",238,238,5,1191916547842972,1191916548408842
"HIP_RUNTIME_API","hipMemcpy",238,238,6,1191916548412677,1191916550217834
"HIP_RUNTIME_API","hipFree",238,238,7,1191916562618151,1191916562789093
"HIP_RUNTIME_API","hipFree",238,238,8,1191916562790923,1191916562836351
To trace HIP compile time APIs, use:
rocprofv3 --hip-compiler-trace < app_relative_path >
Running the above command generates a hip_api_trace.csv file prefixed with the process Id.
$ cat 208_hip_api_trace.csv
"Domain","Function","Process_Id","Thread_Id","Correlation_Id","Start_Timestamp","End_Timestamp"
"HIP_COMPILER_API","__hipRegisterFatBinary",208,208,1,1508780270085955,1508780270096795
"HIP_COMPILER_API","__hipRegisterFunction",208,208,2,1508780270104242,1508780270115355
"HIP_COMPILER_API","__hipPushCallConfiguration",208,208,3,1508780613897816,1508780613898701
"HIP_COMPILER_API","__hipPopCallConfiguration",208,208,4,1508780613901714,1508780613902200
For the description of the fields in the output file, see Output file fields.
HSA trace
The HIP runtime library is implemented with the low-level HSA runtime. HSA API tracing is more suited for advanced users who want to understand the application behavior at the lower level. In general, tracing at the HIP-level is recommended for most users. You are advised to use HSA trace only if you are familiar with HSA runtime.
HSA trace contains the start and end time of HSA runtime API calls and their asynchronous activities.
rocprofv3 --hsa-trace < app_relative_path >
Running the above command generates hsa_api_trace.csv file prefixed with process Id. Note that the contents of this file have been truncated for demonstration purposes.
$ cat 197_hsa_api_trace.csv
"Domain","Function","Process_Id","Thread_Id","Correlation_Id","Start_Timestamp","End_Timestamp"
"HSA_CORE_API","hsa_system_get_major_extension_table",197,197,1,1507843974724237,1507843974724947
"HSA_CORE_API","hsa_agent_get_info",197,197,3,1507843974754471,1507843974755014
"HSA_AMD_EXT_API","hsa_amd_memory_pool_get_info",197,197,5,1507843974761705,1507843974762398
"HSA_AMD_EXT_API","hsa_amd_memory_pool_get_info",197,197,6,1507843974763901,1507843974764030
"HSA_AMD_EXT_API","hsa_amd_memory_pool_get_info",197,197,7,1507843974765121,1507843974765224
"HSA_AMD_EXT_API","hsa_amd_memory_pool_get_info",197,197,8,1507843974766196,1507843974766328
"HSA_AMD_EXT_API","hsa_amd_memory_pool_get_info",197,197,9,1507843974767534,1507843974767641
"HSA_AMD_EXT_API","hsa_amd_memory_pool_get_info",197,197,10,1507843974768639,1507843974768779
"HSA_AMD_EXT_API","hsa_amd_agent_iterate_memory_pools",197,197,4,1507843974758768,1507843974769238
"HSA_CORE_API","hsa_agent_get_info",197,197,11,1507843974771091,1507843974771537
For the description of the fields in the output file, see Output file fields.
Marker trace
In certain situations, such as debugging performance issues in large-scale GPU programs, API-level tracing may be too fine-grained to provide a big picture of the program execution. In such cases, it is helpful to define specific tasks to be traced.
To specify the tasks for tracing, enclose the respective source code with the API calls provided by ROC-TX library. This process is also known as instrumentation. As the scope of code for instrumentation is defined using the enclosing API calls, it is called a range. A range is a programmer-defined task that has a well-defined start and end code scope. You can also fine grain the scope specified within a range using further nested ranges. The rocprofv3 tool also reports the timelines for these nested ranges.
Here is a list of useful APIs for code instrumentation.
roctxMark: Inserts a marker in the code with a message. Creating marks can help you see when a line of code is executed.roctxRangeStart: Starts a range. Ranges can be started by different threads.roctxRangePush: Starts a new nested range.roctxRangePop: Stops the current nested range.roctxRangeStop: Stops the given range.
See how to use rocTX APIs in the MatrixTranspose application below:
roctxMark("before hipLaunchKernel");
int rangeId = roctxRangeStart("hipLaunchKernel range");
roctxRangePush("hipLaunchKernel");
// Lauching kernel from host
hipLaunchKernelGGL(matrixTranspose, dim3(WIDTH/THREADS_PER_BLOCK_X, WIDTH/THREADS_PER_BLOCK_Y), dim3(THREADS_PER_BLOCK_X, THREADS_PER_BLOCK_Y), 0,0,gpuTransposeMatrix,gpuMatrix, WIDTH);
roctxMark("after hipLaunchKernel");
// Memory transfer from device to host
roctxRangePush("hipMemcpy");
hipMemcpy(TransposeMatrix, gpuTransposeMatrix, NUM * sizeof(float), hipMemcpyDeviceToHost);
roctxRangePop(); // for "hipMemcpy"
roctxRangePop(); // for "hipLaunchKernel"
roctxRangeStop(rangeId);
To trace the API calls enclosed within the range, use:
rocprofv3 --marker-trace < app_relative_path >
Running the above command generates marker_api_trace.csv file prefixed with the process Id.
$ cat 210_marker_api_trace.csv
"Domain","Function","Process_Id","Thread_Id","Correlation_Id","Start_Timestamp","End_Timestamp"
"MARKER_CORE_API","before hipLaunchKernel",717,717,1,1520113899312225,1520113899312225
"MARKER_CORE_API","after hipLaunchKernel",717,717,4,1520113900128482,1520113900128482
"MARKER_CORE_API","hipMemcpy",717,717,5,1520113900141100,1520113901483408
"MARKER_CORE_API","hipLaunchKernel",717,717,3,1520113899684965,1520113901491622
"MARKER_CORE_API","hipLaunchKernel range",717,0,2,1520113899682208,1520113901495882
For the description of the fields in the output file, see Output file fields.
Kernel trace
To trace kernel dispatch traces, use:
rocprofv3 --kernel-trace < app_relative_path >
Running the above command generates kernel_trace.csv file prefixed with the process Id.
$ cat 199_kernel_trace.csv
"Kind","Agent_Id","Queue_Id","Kernel_Id","Kernel_Name","Correlation_Id","Start_Timestamp","End_Timestamp","Private_Segment_Size","Group_Segment_Size","Workgroup_Size_X","Workgroup_Size_Y","Workgroup_Size_Z","Grid_Size_X","Grid_Size_Y","Grid_Size_Z"
"KERNEL_DISPATCH",1,139690710949888,15,"matrixTranspose(float*, float*, int)",0,671599758568,671599825328,0,0,4,4,1,1024,1024,1
For the description of the fields in the output file, see Output file fields.
Memory copy trace
To trace memory moves across the application, use:
rocprofv3 –-memory-copy-trace < app_relative_path >
Running the above command generates memory_copy_trace.csv file prefixed with the process Id.
$ cat 197_memory_copy_trace.csv
"Kind","Direction","Source_Agent_Id","Destination_Agent_Id","Correlation_Id","Start_Timestamp","End_Timestamp"
"MEMORY_COPY","HOST_TO_DEVICE",0,1,0,14955949675563,14955950239443
"MEMORY_COPY","DEVICE_TO_HOST",1,0,0,14955952733485,14955953315285
For the description of the fields in the output file, see Output file fields.
Sys trace
This is an all-inclusive option to collect all the above-mentioned traces.
rocprofv3 –-sys-trace < app_relative_path >
Running the above command generates hip_api_trace.csv, hsa_api_trace.csv, kernel_trace.csv, memory_copy_trace.csv, and marker_api_trace.csv (if rocTX APIs are specified in the application) files prefixed with the process Id.
Kernel profiling
The application tracing functionality allows you to evaluate the duration of kernel execution but is of little help in providing insight into kernel execution details. The kernel profiling functionality allows you to select kernels for profiling and choose the basic counters or derived metrics to be collected for each kernel execution, thus providing a greater insight into kernel execution.
For more information on counters available on MI200, refer to the MI200 Performance Counters and Metrics.
Input file
To collect the desired basic counters or derived metrics, mention them in an input file as shown below. The line consisting of the counter or metric names must begin with pmc.
$ cat input.txt
pmc: GPUBusy SQ_WAVES
pmc: GRBM_GUI_ACTIVE
The number of basic counters or derived metrics that can be collected in one run of profiling is limited by the GPU hardware resources. If too many counters or metrics are selected, the kernels need to be executed multiple times to collect the counters or metrics. For multi-pass execution, include multiple rows of pmc in the input file. Counters or metrics in each pmc row can be collected in each run of the kernel.
Kernel profiling output
To supply the input file for kernel profiling, use:
rocprofv3 -i input.txt <app_relative_path>
Running the above command generates ./pmc_n/counter_collection.csv file prefixed with the process Id. For each pmc row, a directory pmc_n containing a counter_collection.csv file is generated where n = 1 for the first row and so on.
Each row of the CSV file is an instance of kernel execution. Here is a truncated version of the output file from pmc_1.
$ cat pmc_1/218_counter_collection.csv
"Correlation_Id","Dispatch_Id","Agent_Id","Queue_Id","Process_Id","Thread_Id","Grid_Size","Kernel_Name","Workgroup_Size","LDS_Block_Size","Scratch_Size","VGPR_Count","SGPR_Count","Counter_Name","Counter_Value"
0,1,1,139892123975680,5619,5619,1048576,"matrixTranspose(float*, float*, int)",16,0,0,8,16,"SQ_WAVES",65536
Output file fields
The various fields or the columns in the output CSV files generated for application tracing and kernel profiling are described here:
| Field | Description |
|---|---|
| Agent_Id | GPU identifier to which the kernel was submitted. |
| Correlation_Id | Unique identifier for correlation between HIP and HSA async calls during activity tracing. |
| Start_Timestamp | Begin time in nanoseconds (ns) when the kernel begins execution. |
| End_Timestamp | End time in ns when the kernel finishes execution. |
| Queue_Id | ROCm queue unique identifier to which the kernel was submitted. |
| Private_Segment_Size | The amount of memory required for the combined private, spill and arg segments for a work-item in bytes. |
| Group_Segment_Size | The amount of group segment memory required by a workgroup in bytes. This does not include any dynamically allocated group segment memory that may be added when the kernel is dispatched. |
| Workgroup_Size | Size of the workgroup as declared by the compute shader. |
| Workgroup_Size_n | Size of the workgroup in the nth dimension as declared by the compute shader, where n = X, Y, or Z. |
| Grid_Size | Number of thread blocks required to launch the kernel. |
| Grid_Size_n | Number of thread blocks in the nth dimension required to launch the kernel, where n = X, Y, or Z. |
| LDS_Block_Size | Thread block size for the kernel's Local Data Share (LDS) memory. |
| Scratch_Size | Kernel’s scratch memory size. |
| SGPR_Count | Kernel's Scalar General-Purpose Register (SGPR) count. |
| VGPR_Count | Kernel's Vector General-Purpose Register (VGPR) count. |
Sample programs
After the ROCm build is installed:
-
Sample programs are installed here:
/opt/rocm/share/rocprofiler-sdk/samples -
rocprofv3tool is installed here:/opt/rocm/bin
To build samples from any directory, run:
cmake -B <build directory> /opt/rocm/share/rocprofiler-sdk/samples -DCMAKE_PREFIX_PATH=/opt/rocm
cmake --build <build directory> --target all --parallel 8
To run the built samples, cd into the <build directory> mentioned in the build commands above and run:
ctest -V