TE-CCL is a tool to generate collective communication schedules for large topologies using a Traffic Engineering-based solver.
TE-CCL takes in a topology and collective (e.g. AllGather) and outputs a schedule (in JSON) detailing data transfer steps for each node that satisfies the demands specified by the collective. In a Traffic Engineering-based approach, TE-CCL encodes the collective communication process into capacity constraints, flow conservation constraints, and destination constraints, and solves a mixed-integer linear program (MILP), with options to convert to an linear program (LP) form or A* form for better scalibility.
Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem
Xuting Liu, Behnaz Arzani, Siva Kesava Reddy Kakarla, Liangyu Zhao, Vincent Liu, Miguel Castro, Srikanth Kandula, and Luke Marshall
SIGCOMM 2024 [https://doi.org/10.1145/3651890.3672249]
@inproceedings{10.1145/3651890.3672249,
author = {Liu, Xuting and Arzani, Behnaz and Kakarla, Siva Kesava Reddy and Zhao, Liangyu and Liu, Vincent and Castro, Miguel and Kandula, Srikanth and Marshall, Luke},
title = {Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem},
year = {2024},
isbn = {9798400706141},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3651890.3672249},
doi = {10.1145/3651890.3672249},
booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
pages = {16–37},
numpages = {22},
keywords = {GPU, collective communication, traffic engineering},
location = {Sydney, NSW, Australia},
series = {ACM SIGCOMM '24}
}
- Install Anaconda and activate an anaconda environment.
- Obtain and install a Gurobi license. After getting the license, follow the steps to install and activate Gurobi.
conda config --add channels http://conda.anaconda.org/gurobi
conda install -c conda-forge gurobi -y
<command to install Gurobi license. e.g. grbgetkey xxxxxx>
In the anaconda environment with Gurobi installed, run
pip install .
TE-CCL takes in a JSON user input file that specifies the topology, Gurobi settings, and model instance parameters. Please refer to Input Data for details.
teccl solve --input_args <input.json>
To generate a schedule for AllGather in the NDv2 topology, run
teccl solve --input_args teccl/examples/sample_inputs/ndv2_input.json
This will generate the schedule file teccl/examples/schedules/ndv2_schedule.json
For detailed examples, please refer to instructions in the examples directory.
Simple topologies (like the example above) can be easily solved using a laptop within seconds. Larger topologies (like 4-chassis ones provided in our examples directory) need 256GB RAM and 1+ hours.
A user input JSON file is consists of three parts: TopologyParams, GurobiParams, and InstanceParams. Detailed explainations of each argument is in input_data.py.
This sepcifies the topology considered. Each topology is defined as a seperate Python file in teccl/topologies.
Parameters for the Gurobi solver.
Parameters for the model instance, including the collective, the choice of objective function and size of epochs.
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