Rocm Profile Data

ROCm Profile Data is a collection of tools for tracing and analyzing gpu related activity on a system. This is represented by a timeline of api calls, app log messages, async gpu operations, and related interactions/dependencies.

Contents:

About
Installation
Quickstart
Tools

About

The ROCm Profiler Data or RPD ecosystem consists of the following:

Collection tools - Profilers capable of detecting and logging gpu activity
File format - A standard file format (based on sqlite3) for collection and analysis tools to work with
Analysis tools - Programs capable of interpreting the profile data in a meaningful way. These can be written in SQL, C/C++, python, etc.

Installation

RPD makes heavy use to SQLite(https://sqlite.org/index.html). You will want to install the runtime and dev packages before preceeding. E.g.

apt-get install sqlite3 libsqlite3-dev

Additional packages required

apt-get install libfmt-dev

There are many tools in the RPD repo. A baseline set can be built and installed via make:

make; make install

This will install python modules that are used to manipulate trace files. It will also build and install the native tracer, rpd_tracer.

Quickstart

Install per the Installation section.
Try each of the Tools below, in order.
You can refer to our step-by-step tutorial in this repository to profile and trace your application. Additionally, we have provided a list of key tables and views defined in the generated rpd file for your reference.

Tools

runTracer.sh

RunTracer.sh is used to launch and trace a program. It is installed in the system path as part of 'make install'. It can trace any process and its subprocesses.

runTracer.sh python exampleWorkload.py

By default the profile will be written to "trace.rpd".

sqlite3

Quick inspection of trace data can be performed with the sqlite3 command line

sqlite3 trace.rpd

sqlite> select count(*) from rocpd_api;
978621
sqlite> select count(*) from rocpd_op;
111899
sqlite> select * from top;
Name                                      TotalCalls  TotalDuration  Ave         Percentage
----------------------------------------  ----------  -------------  ----------  ----------------
Cijk_Alik_Bljk_SB_MT64x128x16_SN_1LDSB0_  3180        3670897        1154        33.1596545434822
Cijk_Alik_Bljk_SB_MT64x128x16_MI32x32x2x  12720       1703806        133         15.3906835540479
Cijk_Alik_Bljk_SB_MT128x128x16_MI32x32x1  3180        1471672        462         13.2937917318908
void at::native::legacy::elementwise_ker  22525       1059802        47          9.57331814908329
void at::native::legacy::elementwise_ker  13593       515243         37          4.65425092430873
...
sqlite> .exit

rpd2tracing.py

Trace data can be viewed in chrome://tracing for visualization. Rpd2tracing.py exports the trace contents as a json file suitable for chrome://tracing or perfetto.

python3 tools/rpd2tracing.py trace.rpd trace.json

Autocop submodule setup

The autocoplite submodule contains a visualization toolkit compatible with trace.rpd files. To use the visualization capabilities of this submodule, from within the main rocmProfileData repository, cd into the autcoplite submodule directory and initialize the submodule:

  git submodule update --init --recursive

This command will initialize, fetch and checkout the submodule to the commit specified in the main repository.

To update the submodule at any time and pull the latest changes, run:

  git submodule update --remote

You can use the provided Makefile to install the dependencies into a python virtual environment named aclite. Simply run:

make install

Alternatively, you can manually install the packages using pip:

pip install -r requirements.txt

To uninstall the packages and remove the virtual environment, you can use the Makefile:

make clean

or manually remove the virtual env:

 rm -rf aclite

Follow the README.md file within the autocoplite submodule for additional instructions and examples for how to run.

ROCm / rocmProfileData

readme