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https://github.com/IntelLabs/pWord2Vec

Parallelizing word2vec in shared and distributed memory
https://github.com/IntelLabs/pWord2Vec

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Parallelizing word2vec in shared and distributed memory

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README 

        
# pWord2Vec



> :warning: **DISCONTINUATION OF PROJECT** - 

> *This project will no longer be maintained by Intel.

> Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.*

> **Intel no longer accepts patches to this project.**

> *If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.*



This is a C++ implementation of word2vec that is optimized on Intel CPUs, particularly, Intel Xeon and Xeon Phi (Knights Landing) processors. It supports the "HogBatch" parallel SGD as described in a NIPS workshop paper "[Parallelizing Word2Vec in Multi-Core and Many-Core Architectures](https://arxiv.org/abs/1611.06172)". It also uses data parallelism to distribute the computation via MPI over a CPU cluster. 



The code is developed based on the [original word2vec](https://code.google.com/archive/p/word2vec/) implementation from Google.



## License

All source code files in the package are under [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).



## Prerequisites

The code is developed and tested on UNIX-based systems with the following software dependencies:



- [Intel Compiler](https://software.intel.com/en-us/qualify-for-free-software) (The code is optimized on Intel CPUs)

- OpenMP (No separated installation is needed once Intel compiler is installed)

- MKL (The latest version "16.0.0 or higher" is preferred as it has been improved significantly in recent years)

- MPI library, with multi-threading support (Intel MPI, MPICH2 or MVAPICH2 for distributed word2vec only)

- [HyperWords](https://bitbucket.org/omerlevy/hyperwords) (for model accuracy evaluation)

- Numactl package (for multi-socket NUMA systems)



## Environment Setup

* Install Intel C++ development environment (i.e., Intel compiler, OpenMP, MKL "16.0.0 or higher" and iMPI. [free copies](https://software.intel.com/en-us/qualify-for-free-software) are available for some users)

* Enable Intel C++ development environment

```

source /opt/intel/compilers_and_libraries/linux/bin/compilervars.sh intel64 (please point to the path of your installation)

source /opt/intel/impi/latest/compilers_and_libraries/linux/bin/compilervars.sh intel64 (please point to the path of your installation)

```

* Install numactl package

```

sudo yum install numactl (on RedHat/Centos)

sudo apt-get install numactl (on Ubuntu)

```



## Quick Start

1. Download the code: ```git clone https://github.com/IntelLabs/pWord2Vec```

2. Run .\install.sh to build the package (e.g., it downloads hyperwords and compiles the source code.)  

Note that this installation will try to produce two binaries: pWord2Vec and pWord2Vec_mpi. If you are only interested in the non-mpi version of w2v, you don't need to set up mpi and the compilation will fail on building pWord2Vec_mpi of course. But you can still use the non-mpi binary for the rest of single machine demos.

3. Download the data: ```cd data; .\getText8.sh or .\getBillion.sh```

4. Run the demo script: ```cd sandbox; ./run_single_text8.sh (for single machine demo) or ./run_mpi_text8.sh (for distributed w2v demo)```

5. Run the code on the 1-billion-word-benchmark: ```cd billion; ./run_single.sh (for single machine w2v) or ./run_mpi.sh (for distributed w2v) (please set ncores=number of logical cores of your machine)```

6. Evaluate the models: ```cd sandbox; ./eval.sh or cd billion; ./eval.sh```



## Reference



[Parallelizing Word2Vec in Shared and Distributed Memory](https://ieeexplore.ieee.org/document/8663393), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), Volume 30, Issue 9, Pages 2090-2100, Sept. 1 2019. 



[Parallelizing Word2Vec in Multi-Core and Many-Core Architectures](https://arxiv.org/abs/1611.06172), NIPS workshop on Efficient Methods for Deep Neural Networks, Dec. 2016.



For questions and bug reports, you can reach me at https://grid.cs.gsu.edu/~sji/