GraphRTX: Lighting the Way to Scalable Graph Analytics

Modern GPU architectures are powerful platforms for accelerating database processing, including graph analytics. However, due to irregular access patterns in graphs caused by properties such as power-law distribution of vertex degrees, pure SMT execution models can result in resource under-utilization and poor memory coalescing. Modern GPUs address this issue by using heterogeneous accelerators for different types of workload. Among these are ray tracing (RT) cores, which perform hardware-accelerated spatial index traversal, overcoming similar challenges of irregular memory access. By offloading traversal and intersection to fixed-function hardware, RT cores align well with graph traversal patterns. However, there is still no unified graph representation for RT that avoids costly index rebuilding across algorithms. To address this gap, we introduce GraphRTX, a framework that leverages the RT cores of modern NVIDIA GPUs to accelerate graph algorithms. GraphRTX employs a unified graph representation that maps graphs to bounding volume hierarchies (BVHs) and a ray-based query abstraction and enables multiple algorithms to operate on the same model. We also propose a hybrid execution model that combines RT acceleration with GPU thread-level parallelism. Experiments on real-world datasets demonstrate that GraphRTX matches or outperforms state-of-the-art baselines, offering lower BVH (re)build overhead and a smaller memory footprint.