惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 叶小钗
Last Week in AI
Last Week in AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
雷峰网
雷峰网
GbyAI
GbyAI
Hugging Face - Blog
Hugging Face - Blog
N
Netflix TechBlog - Medium
博客园 - 聂微东
Y
Y Combinator Blog
罗磊的独立博客
博客园_首页
小众软件
小众软件
有赞技术团队
有赞技术团队
爱范儿
爱范儿
F
Fortinet All Blogs
C
Check Point Blog
Google DeepMind News
Google DeepMind News
云风的 BLOG
云风的 BLOG
Apple Machine Learning Research
Apple Machine Learning Research
M
MIT News - Artificial intelligence
月光博客
月光博客
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 司徒正美
aimingoo的专栏
aimingoo的专栏

cs.DB updates on arXiv.org

Block-Sphere Vector Quantization GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction CogScale: Scalable Benchmark for Sequence Processing TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks Gaussian Relational Graph Transformer Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift A Horn extension of DL-Lite with NL data complexity 3D Primitives are a Spatial Language for VLMs Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance A CAP-like Trilemma for Large Language Models: Correctness, Non-bias, and Utility under Semantic Underdetermination EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Toward Multi-Database Query Reasoning for Text2Cypher Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations PrepBench: How Far Are We from Natural-Language-Driven Data Preparation? Anatomy of a Query: W5H Dimensions and FAR Patterns for Text-to-SQL Evaluation Building informative materials datasets beyond targeted objectives
Taking the Leap: Efficient and Reliable Fine-Grained NUMA...
Felix Schuhknecht, Nick Rassau · 2026-02-05 · via cs.DB updates on arXiv.org

Modern multi-socket architectures offer a single virtual address space, but physically divide main-memory across multiple regions, where each region is attached to a CPU and its cores. While this simplifies the usage, developers must be aware of non-uniform memory access (NUMA), where an access by a thread running on a core-local NUMA region is significantly cheaper than an access from a core-remote region. Obviously, if query answering is parallelized across the cores of multiple regions, then the portion of the database on which the query is operating should be distributed across the same regions to ensure local accesses. As the present data placement might not fit this, migrating pages from one NUMA region to another can be performed to improve the situation. To do so, different options exist: One option is to rely on automatic NUMA balancing integrated in Linux, which is steered by the observed access patterns and frequency. Another option is to actively trigger migration via the system call move_pages(). Unfortunately, both variants have significant downsides in terms of their feature set and performance. As an alternative, we propose a new user-space migration method called page_leap() that can perform page migration asynchronously at a high performance by exploiting features of the virtual memory subsystem. The method is (a) actively triggered by the user, (b) ensures that all pages are eventually migrated, (c) handles concurrent writes correctly, (d) supports pooled memory, (e) adaptively adjusts its migration granularity based on the workload, and (f) supports both small pages and huge pages.