Depth First Search (DFS) is a fundamental graph traversal algorithm with broad applications. While existing work-stealing DFS approaches achieve strong performance on CPUs, mapping them to modern GPUs faces three major challenges: (1) limited shared memory cannot accommodate deep stacks, (2) frequent stack operations hinder efficient intra-block execution, and (3) irregular workloads complicate scalable inter-block execution.

In this paper, we propose DiggerBees, a GPU-optimized parallel DFS algorithm with hierarchical block-level stealing, consisting of three components. First, we introduce a two-level stack structure to mitigate shared memory limitations. Second, we employ warp-level DFS with intra-block work stealing to enable efficient execution within a block. Third, we implement inter-block work stealing to achieve scalable execution across blocks and sustain high parallelism. Experimental results on the latest NVIDIA GPUs show that DiggerBees outperforms existing DFS approaches, CKL-PDFS, ACR-PDFS, and NVG-DFS, achieving average speedups of $1.37\times$, $1.83\times$, and $30.18\times$, respectively. Moreover, DiggerBees even surpasses high-performance GPU BFS implementations on graphs with deep and narrow traversal paths, and scales efficiently across GPU generations.