  Counting (p,q)-bicliques in bipartite graphs poses a foundational challenge
with broad applications, from densest subgraph discovery in algorithmic
research to personalized content recommendation in practical scenarios. Despite
its significance, current leading (p,q)-biclique counting algorithms fall
short, particularly when faced with larger graph sizes and clique scales.
Fortunately, the problem's inherent structure, allowing for the independent
counting of each biclique starting from every vertex, combined with a
substantial set intersections, makes it highly amenable to parallelization.
Recent successes in GPU-accelerated algorithms across various domains motivate
our exploration into harnessing the parallelism power of GPUs to efficiently
address the (p,q)-biclique counting challenge. We introduce GBC (GPU-based
Biclique Counting), a novel approach designed to enable efficient and scalable
(p,q)-biclique counting on GPUs. To address major bottleneck arising from
redundant comparisons in set intersections (occupying an average of 90% of the
runtime), we introduce a novel data structure that hashes adjacency lists into
truncated bitmaps to enable efficient set intersection on GPUs via bit-wise AND
operations. Our innovative hybrid DFS-BFS exploration strategy further enhances
thread utilization and effectively manages memory constraints. A composite load
balancing strategy, integrating pre-runtime and runtime workload allocation,
ensures equitable distribution among threads. Additionally, we employ vertex
reordering and graph partitioning strategies for improved compactness and
scalability. Experimental evaluations on eight real-life and two synthetic
datasets demonstrate that GBC outperforms state-of-the-art algorithms by a
substantial margin. In particular, GBC achieves an average speedup of 497.8x,
with the largest instance achieving a remarkable 1217.7x speedup when p = q =
8.
