SRDatalog implements worst-case optimal joins on GPUs for Datalog using columnar storage and skew-mitigation techniques, achieving 21-47x speedups on program-analysis workloads while avoiding asymptotic blowups from binary joins.
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Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.
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Scaling Worst-Case Optimal Datalog to GPUs
SRDatalog implements worst-case optimal joins on GPUs for Datalog using columnar storage and skew-mitigation techniques, achieving 21-47x speedups on program-analysis workloads while avoiding asymptotic blowups from binary joins.
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Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.