CallFlow is an interactive visual analysis tool that provides a high-level overview of CCTs together with semantic refinement operations to progressively explore the CCTs.
CallFlow is structured as three components:
- A Python package
callflow
that provides functionality to load and manipulate callgraphs. - A D3 based
app
for visualization. - A python
server
to support the visualization client.
The callflow
(python package) requires python (>= 3.6) and pip (>= 20.1.1). Other dependencies are checked/installed during the installation of callflow
using setup.py
.
python3 setup.py install --prefix PATH/TO/INSTALL
Sample data and examples are provided in the data
and examples
directories.
The first step is to process the raw datasets to use with CallFlow. This preprocessing typically entails some filtering and aggregation of data to produce the reduced graphs at desired granularity. The parameters of the preprocessing are provided through a config file (see examples of config files in the sample data directories).
The processing of profiles generates a .callflow
directory in the ${save_path}, which is provided either in the callflow.config.json
or --save_path attribute. If not provided, the .callflow
directory will be placed in the data folder.
/PATH/TO/INSTALL/callflow_server --data-dir {PATH_TO_DATA_DIRECTORY}
By default, the application would run on port, 5000. If the port needs to be changed, please set the environment variables using,
export CALLFLOW_APP_PORT=<port_number>
The callflow app
requires node.js (>= 13.7.0) and npm
(>= 6.13.7). If there is an older version of node
installed, install nvm and use the following command to change version.
nvm use 13.7.0
The app
and its dependencies can be installed as follows.
cd app
npm install
To start the app
,
npm run serve
To build the app
,
npm run build
The basic architecture diagram can be found here.
Any published work that utilizes this software should include the following references:
For CallFlow 1.0 that supports a single callgraph, cite:
- Huu Tan Nguyen, Abhinav Bhatele, Nikhil Jain, Suraj P. Kesavan, Harsh Bhatia, Todd Gamblin, Kwan-Liu Ma, Peer-Timo Bremer. Visualizing Hierarchical Performance Profiles of Parallel Codes using CallFlow. IEEE Transactions on Visualization and Computer Graphics, 2019. doi:10.1109/TVCG.2019.2953746.
For Callflow v1.1 that supports comparative visualization (ensembles of callgraphs), cite:
- Suraj P. Kesavan, Harsh Bhatia, Abhinav Bhatele, Todd Gamblin, Peer-Timo Bremer, Kwan-Liu Ma. Scalable Comparative Visualization of Ensembles of Call Graphs. arXiv:2007.01395.
CallFlow is released under MIT license. See the LICENSE file for details.
LLNL-CODE-740862
.
Written by Suraj P. Kesavan ([email protected]) and Huu Tan Nguyen ([email protected]).
Copyright (c) 2020, Lawrence Livermore National Security, LLC. Produced at the Lawrence Livermore National Laboratory. All rights reserved.