GPU-based parallel algorithm for generating massive scale-free networks using the preferential attachment model
A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale-free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale-free network of two billion edges in less than 3 seconds.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1509591
- Resource Relation:
- Conference: Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining - Boston, Massachusetts, United States of America - 12/11/2017 5:00:00 AM-12/14/2017 5:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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