  Maximal Biclique Enumeration (MBE) holds critical importance in graph theory
with applications extending across fields such as bioinformatics, social
networks, and recommendation systems. However, its computational complexity
presents barriers for efficiently scaling to large graphs. To address these
challenges, we introduce cuMBE, a GPU-optimized parallel algorithm for MBE.
Utilizing a unique data structure, called compact array, cuMBE eradicates the
need for recursion, thereby significantly minimizing dynamic memory
requirements and computational overhead. The algorithm utilizes a hybrid
parallelism approach, in which GPU thread blocks handle coarse-grained tasks
associated with part of the search process. Besides, we implement three
fine-grained optimizations within each thread block to enhance performance.
Further, we integrate a work-stealing mechanism to mitigate workload imbalances
among thread blocks. Our experiments reveal that cuMBE achieves an geometric
mean speedup of 4.02x and 4.13x compared to the state-of-the-art serial
algorithm and parallel CPU-based algorithm on both common and real-world
datasets, respectively.
