Fast Community Detection in Graphs with Infomap Method using Accelerated Sparse Accumulation

Information-theoretic community discovery method (popularly known as Infomap) is known for delivering better quality results in the Lancichinetti-Fortunato-Radicchi (LFR) benchmark compared to modularity-based algorithms. Parallel algorithms have been developed for Infomap due to the computational challenge of analyzing massive graphs resulting from the tremendous growth of information in bio-sciences, social sciences, business, and other domains. The state-of-the-art techniques on information-theoretic community discovery use hash tables for storing vertex neighborhood flow information, which can be computationally expensive due to collision handling operations and CPU branch mispredictions. The Accelerated Sparse Accumulation (ASA) hardware accelerator for hash accumulation has been developed recently for sparse matrix-matrix multiplication (SpGEMM). We generalize the interface of the ASA accelerator and demonstrate that for state-of-the-art parallel Infomap, the accelerator for hash accumulation with fast on-chip memory can overcome the performance bottlenecks of software hash tables and can achieve a speedup of 5.56x while reducing the number of branch mispredictions by 59%, the CPI rate by 21%, and the total number of instructions by 24%.