Explores techniques for accelerating Datalog query evaluation on GPU hardware, targeting the fixpoint computation at its core.
Key Takeaways
Datalog evaluation relies on iterative fixpoint computation over relations, a workload that maps naturally to bulk parallel operations on GPUs.
The central challenge is handling recursive rules efficiently: GPU memory access patterns and synchronization differ sharply from CPU-optimized Datalog engines like Souffle.
GPU acceleration of Datalog is directly relevant to program analysis, static analysis pipelines, knowledge graph reasoning, and Datalog-as-query-engine use cases.
Parallelizing relation joins and union operations at scale is where GPU throughput gains would be most pronounced over CPU baselines.