Although GPGPUs are traditionally used to accelerate workloads with regular control and memory-access structure, recent work has shown that GPGPUs can also achieve significant speedups on more irregular algorithms. Data-driven implementations of irregular algorithms are algorithmically more efficient than topology-driven implementations, but issues with memory contention and memory-access irregularity can make the former perform worse in certain cases. In this paper, we propose a novel fine-grain hardware work list for GPGPUs that addresses the weaknesses of data-driven implementations. We detail multiple work redistribution schemes of varying complexity that can be employed to improve load balancing. Furthermore, a virtualization mechanism supports seamless work spilling to memory. A convenient shared work list software API is provided to simplify using our proposed mechanisms when implementing irregular algorithms. We evaluate challenging irregular algorithms from the Lonestar GPU benchmark suite on a cycle-level simulator. Our findings show that data-driven implementations running on a GPGPU using the hardware work list outperform highly optimized software-based implementations of these benchmarks running on a baseline GPGPU with speedups ranging from 1.2 -- 2.4× and marginal area overhead.