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.
Yogatama, Weiwei Gong, and Xiangyao Yu
3 Pith papers cite this work, alongside 28 external citations. Polarity classification is still indexing.
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LLM-synthesized GPU query kernels outperform engineered engines by 7.4x, but a portable SYCL engine with lifted optimizations closes the gap to 1.27x, suggesting engineering remains preferable on GPUs.
Spectral aggregate tests prune up to 51% of candidates in CSM but leave enumeration intermediates unchanged beyond initial bindings across tested workloads.
citing papers explorer
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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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From Custom-Fit to Portable: Bridging the Gap Between Synthesized and Engineered GPU Query Execution
LLM-synthesized GPU query kernels outperform engineered engines by 7.4x, but a portable SYCL engine with lifted optimizations closes the gap to 1.27x, suggesting engineering remains preferable on GPUs.
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Can Aggregate Invariants Accelerate Continuous Subgraph Matching? Limits, Laws, and a Dynamic Spectral Index
Spectral aggregate tests prune up to 51% of candidates in CSM but leave enumeration intermediates unchanged beyond initial bindings across tested workloads.