The article explains how sampling serves as the cost‑fidelity knob in distributed tracing, why full sampling collapses at ten‑million QPS, compares head‑based, tail‑based, and intelligent adaptive strategies, and shows how tools like OpenTelemetry, Jaeger, and AWS X‑Ray implement these approaches.
The SIGGRAPH 2026 paper “Generative 3D Gaussians with Learned Density Control” introduces Density‑Sampled Gaussians (DeG), a differentiable framework that lets a model learn where to place Gaussian splats by sampling from a learned spatial density, enabling arbitrary‑budget, non‑uniform 3D representations with higher quality per cost.
The article dissects AI safety testing bottlenecks and presents four optimization dimensions—testing paradigm, data generation, execution architecture, and feedback loop—offering concrete techniques such as risk‑aware input filtering, gradient‑cache reuse, heterogeneous parallelism, and adaptive sampling that together cut testing time by several folds.
This article explains how adaptive sampling reduces the performance and storage impact of full‑link tracing by dynamically adjusting the sampling rate based on application QPS, outlines the mathematical model, discusses implementation details such as warm‑up, QPS lag, concurrency, and provides a Java reference sampler.
