Risk-Aware GPU-Assisted Cardinality Estimation for Cost-Based Query Optimizers

Cardinality estimation is a cornerstone of cost-based optimizers (CBOs), yet real-world workloads often violate the assumptions behind static statistics, degrading decision stability and increasing plan flip rates. We empirically characterize failures caused by stale statistics, skew, join correlations, hidden distributions in bind variables, and sampling bias, and quantify the overhead and break-even points of hardware-accelerated measurement. We propose GACE (GPU-Assisted Cardinality Estimation), a hybrid auxiliary architecture that augments rather than replaces the optimizer. GACE selectively invokes GPU-based measurement only in risky intervals via a Risky Gate that detects estimation uncertainty, and a GPU Measurement Engine that performs high-speed probing with explicit cost accounting for the measurement itself. This design preserves low overhead in stable regions while improving plan stability and reducing tail latency (P99) in problematic scenarios.