Scale-Free

Parallel/Distributed implementation (1000s of GPUs), Graph algorithms, Graph Generation on multiple GPUs, Community detection-based solutions

We present a message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. The algorithms have been exercised in scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

In this paper, we present an agent based model (and a scalable approximation of it) in a closely related spirit. The central feature of this model is that wealth enables an individual to secure more wealth. Using historical data, we initialize the model with US wealth shares in 1988 and show that the model tracks wealth share changes from 1988 to 2012. Simulations to 2088 project that the top 0.01% of the population will possess more than 70% of the total wealth in the economy.

A novel parallel algorithm, cuPPA, is presented for generating random scale-free networks using the preferential-attachment model. 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.

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.