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.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
fields
cs.DB 4years
2026 4roles
background 3polarities
background 3representative citing papers
UFS is an eBPF unfair scheduler that improves time-sensitive task throughput by up to 2X and halves tail latency in mixed PostgreSQL workloads by limiting background tasks to idle capacity and using hints to prevent priority inversion.
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.
citing papers explorer
-
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.
-
Unfair by design: eBPF-based scheduling of mixed database workloads
UFS is an eBPF unfair scheduler that improves time-sensitive task throughput by up to 2X and halves tail latency in mixed PostgreSQL workloads by limiting background tasks to idle capacity and using hints to prevent priority inversion.
-
NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
-
To GPU or Not to GPU: Vector Search in Relational Engines
Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.