Explore a conference talk from USENIX ATC '23 that delves into the implementation of iterative relational algebra on GPUs for high-performance data analytics. Learn about the challenges and solutions in developing GPU-based hash-join implementations for declarative languages like Datalog. Discover novel techniques such as open-addressing-based hash tables, operator fusing, and deduplication variants that enhance performance. Compare the presented approach to existing CPU-based and GPU-based solutions, with insights into significant performance gains achieved in transitive closure computations. Gain valuable knowledge about the potential of GPU acceleration in fields like graph mining, program analysis, and social media analytics.