Biological networks capture structural or functional properties of relevant 
entities such as molecules, proteins or genes. Characteristic examples are gene 
expression networks or protein-protein interaction networks, which hold 
information about functional affinities or structural similarities. Such 
networks have been expanding in size due to increasing scale and abundance of 
biological data. While various clustering algorithms have been proposed to find 
highly connected regions, Markov Clustering (MCL) has been one of the most 
successful approaches to cluster sequence similarity or expression networks. 
Despite its popularity, MCL's scalability to cluster large datasets still 
remains a bottleneck due to high running times and memory demands. Here, we 
present High-performance MCL (HipMCL), a parallel implementation of the original 
MCL algorithm that can run on distributed-memory computers. We show that HipMCL 
can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million 
nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory 
environments, HipMCL clusters large-scale networks several orders of magnitude 
faster than MCL and enables clustering of even bigger networks. HipMCL is based 
on MPI and OpenMP and is freely available under a modified BSD license.