FTIO captures periodic I/O using frequency techniques. Many high-performance computing (HPC) applications perform their I/O in bursts following a periodic pattern. Predicting such patterns can be very efficient for I/O contention avoidance strategies, including burst buffer management, for example. FTIO allows offline detection and online prediction of periodic I/O phases. FTIO uses the discrete Fourier transform (DFT), combined with outlier detection methods to extract the dominant frequency in the signal. Additional metrics gauge the confidence in the output and tell how far from being periodic the signal is. A complete description of the approach is provided here.
This repository provides two main Python-based tools:
ftio: Uses frequency techniques and outlier detection methods to find the period of I/O phasespredictor: Implements the online version of FTIO. It reinvokes FTIO whenever new traces are appended to the monitored file. See online prediction for more details. We recommend using TMIO to generate the file with the I/O traces.
Other tools:
ioplot: Generates interactive plots in HTMLioparse: Parses and merges several traces to an Extra-P supported format. This allows one to examine the scaling behavior of the monitored metrics. Traces generated by FTIO (frequency modls), TMIO (msgpack, json and jsonl) and other tools (Darshan, Recorder, and TAU Metric Proxy) are supported.
Table of Contents
Join the Slack channel or see the latest updates here: Latest News
FTIO is available on PYPI and can be easily installed via pip. For the latest GitHub version, FTIO can be installed either automatically or manually. As a prerequisite,
for the virtual environment, python3.11-venv is needed, which can be installed on Ubuntu, for example, with:
apt install python3.11-venvIf you want to contribute to the code, we advise that you install FTIO as mentioned under contributing.
FTIO is installed by default in a virtual environment. For the automated installation, simply execute the command:
# clone FTIO
git clone https://github.com/tuda-parallel/FTIO.git
cd FTIO
# uses by default python3
make install
# or using a specific python version,
# which is often needed on a cluster
make install PYTHON=python3.12This generates a virtual environment in the current directory, sources .venv/bin/activate, and installs FTIO as a module.
If you are working on an HPC cluster, you first need to load the Python module (e.g., module load python/3.12) and eventually add ~/.loacl/bin to your PATH (e.g., export PATH=$PATH:~/.local/bin) in case it's not there yet.
If you don't need a dedicated environment, just call:
make ftio PYTHON=python3Create a virtual environment if needed and activate it:
git clone https://github.com/tuda-parallel/FTIO.git
cd FTIO
python3 -m venv .venv
source .venv/bin/activateInstall all tools provided in this repository simply by using pip:
pip install .Note
You need to activate the environment to use ftio and the other tools using:
source path/to/venv/bin/activateNote
Note there are currently issues with pyDarshan on Mac and windows, that can be solved as mentioned here
FTIO is available on PYPI and can be easily installed via pip:
pip install ftio-hpcNote
Note there are currently issues with pyDarshan on Mac and windows, that can be solved as mentioned here
For installation instructions see installation.
To call ftio on a file execute:
ftio filename.extensionThere are three options to use ftio and predictor:
- Provide a supported file format to the tool. Supported extensions are
json,jsonLines,msgpack, anddarshan. For recorder, you provide the path to the folder instead offilename.extension. For more on the input format see supported file formats. There is also an option to provide a custom format. - Use the API. This is particularly good if you just want to experiment with the tool, or directly jump into using it with as little effort as possible.
- Send TCP messages over ZeroMQ (ZMQ) to the tools as described here. There is also an API example with ZMQ and GekkoFS here. Usually,
predictoris used with ZMQ, as it makes little sense to useftiowith this option.
In all cases, various options can be provided to ftio and predictor. To see all available command line arguments, call:
ftio -h
usage: ftio [-h] [-m MODE] [-r RENDER] [-f FREQ] [-ts TS] [-te TE] [-tr TRANSFORMATION] [-e ENGINE]
[-o OUTLIER] [-le LEVEL] [-t TOL] [-d] [-nd] [-re] [--no-reconstruction] [-p] [-np] [-c] [-w]
[-fh FREQUENCY_HITS] [-v] [-s] [-ns] [-a] [-na] [-i] [-ni] [-x DXT_MODE] [-l LIMIT]
files [files ...]
There are several options available to enhance the frequency predictions from ftio. In the standard mode, the DFT is used in combination with an outlier detection method. Additionally, autocorrelation can be used to further increase the confidence in the results:
- DFT + outlier detection (Z-score, DB-Scan, Isolation forest, peak detection, or LOF)
- Optionally: Autocorrelation + Peak detection (
-cflag) - If step 2. is performed, the results from both predictions are merged automatically
See offline detection for more details.
Several flags can be specified. The most relevant settings are:
| Flag | Description |
|---|---|
| file | file, file list (file 0 ... file n), folder, or folder list (folder 0.. folder n) containing traces (positional argument) |
| -h, --help | show this help message and exit |
| -m MODE, --mode MODE | if the trace file contains several I/O modes, a specific mode can be selected. Supported modes are: write_async, read_async, write_sync, read_sync |
| -r RENDER, --render RENDER | specifies how the plots are rendered. Either dynamic (default) or static |
| -f FREQ, --freq FREQ | specifies the sampling rate with which the continuous signal is discretized (default=10Hz). This directly affects the highest captured frequency (Nyquist). The value is specified in Hz. In case this value is set to -1, the auto mode is launched which sets the sampling frequency automatically to the smallest change in the bandwidth detected. Note that the lowest allowed frequency in the auto mode is 2000 Hz |
| -ts TS, --ts TS | Modifies the start time of the examined time window |
| -te TE, --te TE | Modifies the end time of the examined time window |
| -tr TRANSFORMATION, --transformation TRANSFORMATION | specifies the frequency technique to use. Supported modes are: dft (default), wave_disc, and wave_cont |
| -e ENGINE, --engine ENGINE | specifies the engine used to display the figures. Either plotly (default) or mathplotlib can be used. Plotly is used to generate interactive plots as HTML files. Set this value to no if you do not want to generate plots |
| -o OUTLIER, --outlier OUTLIER | outlier detection method: Z-score (default), DB-Scan, Isolation_forest, or LOF |
| -le LEVEL, --level LEVEL | specifies the decomposition level for the discrete wavelet transformation (default=3). If specified as auto, the maximum decomposition level is automatic calculated |
| -t TOL, --tol TOL | tolerance value |
| -d, --dtw | performs dynamic time wrapping on the top 3 frequencies (highest contribution) calculated using the DFT if set (default=False) |
| -re, --reconstruction | plots reconstruction of top 10 signals on figure |
| -np, --no-psd | if set, replace the power density spectrum (a*a/N) with the amplitude spectrum (a) |
| -c, --autocorrelation | if set, autocorrelation is calculated in addition to DFT. The results are merged to a single prediction at the end |
| -w, --window_adaptation | online time window adaptation. If set to true, the time window is shifted on X hits to X times the previous phases from the current instance. X corresponds to frequency_hits |
| -fh FREQUENCY_HITS, --frequency_hits FREQUENCY_HITS | specifies the number of hits needed to adapt the time window. A hit occurs once a dominant frequency is found |
| -v, --verbose | sets verbose on or off (default=False) |
| -x DXT_MODE, --dxt_mode DXT_MODE | select data to extract from Darshan traces (DXT_POSIX or DXT_MPIIO (default)) |
| -l LIMIT, --limit LIMIT | max ranks to consider when reading a folder |
predictor has the same syntax as ftio. All arguments that are available for ftio are also available for predictor.
There is a 8.jsonl file provided for testing under examples.
On your system, navigate to the folder examples/tmio/JSONL and call:
ftio 8.jsonlSeveral examples are provided under examples. See also the examples provided here for the different file formats.
Alternatively, the artifact folder contains several instructions and examples traces from the FTIO paper that can be simply downloaded as described here.
As ftio supports Darshan traces, you could download also traces from https://hpcioanalysis.zdv.uni-mainz.de/ and execute FTIO on them as described here.
For an online example with predictor, you can follow the instructions here for HACC-IO.
[!note] If you are a student from TU Darmstadt, kindly see these instructions.
- Visit the FTIO GitHub repository.
- Click the Fork button in the top-right corner to create a copy of the repository under your GitHub account.
Clone the forked repository to your local machine:
git clone https://github.com/<your-username>/FTIO.gitReplace <your-username> with your GitHub username.
cd FTIOCompile the project using the make debug command:
# allows to directly test the changes made
make debug This will generate a debug build of the project, useful for development and troubleshooting.
To stay up-to-date with the latest changes from the main repository:
git remote add upstream https://github.com/tuda-parallel/FTIO.git
git fetch upstream
git merge upstream/mainBefore starting your work, create an issue on the repository to describe the feature, bug fix, or enhancement you plan to implement. This helps us track contributions and avoids duplicate work.
- Go to the Issues tab in the FTIO repository.
- Click New Issue and provide a clear title and description.
- Label the issue appropriately (e.g.,
enhancement,bug, orquestion).
-
Create a new branch for your changes:
git checkout -b <your-feature-branch>
Replace
<your-feature-branch>with a descriptive name for your branch. -
Make your desired changes and commit them:
git add . git commit -m "Description of your changes"
Push your changes to your forked repository:
git push origin <your-feature-branch>- Navigate to the original FTIO repository on GitHub.
- Click the Pull Requests tab, then click New Pull Request.
- Set the target branch to
development:- Base Repository:
tuda-parallel/FTIO - Base Branch:
development - Compare Branch:
<your-feature-branch>
- Base Repository:
- Provide a detailed description of your changes, referencing the issue you created earlier (e.g.,
Fixes #123). - Submit your pull request and wait for feedback from the maintainers.
We look forward to your contributions! 🎉
- Ahmad Tarraf: [email protected]
Distributed under the BSD 3-Clause License. See LICENCE for more information.
Authors:
- Ahmad Tarraf
This work is a result of cooperation between the Technical University of Darmstadt and INRIA in the scope of the EuroHPC ADMIRE project.
@inproceedings{AT24_ftio,
author={Tarraf, Ahmad and Bandet, Alexis and Boito, Francieli and Pallez, Guillaume and Wolf, Felix},
booktitle={2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
title={Capturing periodic {I/O} using frequency techniques},
month=may,
year={2024},
pages={465-478},
publisher = {IEEE},
doi={10.1109/IPDPS57955.2024.00048}
}
-
A. Tarraf, A. Bandet, F. Boito, G. Pallez, and F. Wolf, “Capturing Periodic I/O Using Frequency Techniques,” in 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS), San Francisco, CA, USA, May 2024, pp. 1–14.
-
A. Tarraf, A. Bandet, F. Boito, G. Pallez, and F. Wolf, “FTIO: Detecting I/O periodicity using frequency techniques.” arXiv preprint arXiv:2306.08601 (2023).
