More Bang for Your Buck(et): Fast and Space-Efficient Hardware-Accelerated Coarse-Granular Indexing on GPUs
Justus Henneberg, Felix Schuhknecht, Rosina Kharal, Trevor Alexander Brown · 2025
In recent work, it has been shown that NVIDIA's ray tracing cores on RTX video cards can be exploited to realize hardware-accelerated lookups for GPU-resident database indexes. This is done by materializing all keys as triangles in a 3D scene. Lookups are performed by firing rays into the scene and utilizing the built-in index structure to detect collisions with triangles in a hardware-accelerated fashion. While this approach, called RTIndeX (or RX for short), is indeed promising, it currently suffers from three limitations: (1) significant memory overhead per key, (2) slow range lookups, and (3) poor updateability. In this work, we show that all three problems can be tackled by a single design change: Generalizing RX to become a coarse-granular index cgRX, which no longer indexes individual keys, but key buckets. We show that representing buckets in 3D space such that the lookup of a key is performed both correctly and efficiently is highly nontrivial and requires a careful orchestration of positioning triangles and firing rays in a specific sequence. Our experimental evaluation shows that cgRX offers the most bang for the buck(et) by providing a up to 6.9 x higher ratio of throughput to memory footprint than comparable baselines (that support range lookups). At the same time, cgRX improves the range-lookup performance over RX by up to 15 x and offers practical updatability that is up to 5.6x faster than rebuilding from scratch