💻 programming

FlagCX

FlagCX is a cross-chip communication library.

#AI
#Open source
#high performance computing
#Cross-chip communication
FlagCX

Product Details

FlagCX is a scalable and adaptive cross-chip communication library developed with support from the Beijing Academy of Artificial Intelligence (BAAI). It is part of the FlagAI-Open open source initiative, which aims to promote the open source ecosystem of AI technology. FlagCX utilizes the native collective communication library to fully support single-chip communication on different platforms. Supported communication backends include NCCL, IXCCL and CNCL.

Main Features

1
Support single-chip communication on different platforms.
2
Provides full support for cross-chip communication.
3
Leverage native collective communication libraries such as NCCL, IXCCL and CNCL.
4
Provides construction and testing guides to facilitate developers to use and test.
5
Supports custom build paths and installation paths for device runtime and communication libraries.
6
Provides a variety of test parameters to adapt to different performance testing needs.

How to Use

1
1. Clone the repository: Use the git clone command to clone the FlagCX code base.
2
2. Build the library: Enter the FlagCX directory and use the make command plus the corresponding platform flag to build.
3
3. Test performance: In the test/perf directory, use the make command to build the test program and run the test program, for example, use ./test_allreduce -b 128M -e 8G -f 2 for performance testing.
4
4. Adjust test parameters: Adjust test parameters as needed, for example, use -b, -e, -f parameters to set the size range and increment of test data.
5
5. View the documentation: Visit FlagCX’s GitHub page and view the README and LICENSE files for more usage and licensing information.
6
6. Contribute code: If necessary, developers can contribute code to FlagCX to jointly promote the development of the project.

Target Users

FlagCX's target audience is AI developers, researchers, and enthusiasts who require efficient data communication on different hardware platforms. This product is suitable for users who require large-scale data processing and high-performance computing due to its open source nature and support for multiple communication backends.

Examples

Used for data parallel processing in AI model training.

Efficient communication between multiple chips in a deep learning framework.

As an open source project, it is used by developers and researchers around the world for various AI-related research and development work.

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› AI model
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