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

OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
G
Google Developers Blog
雷峰网
雷峰网
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
U
Unit 42
B
Blog RSS Feed
博
博客园 - 【当耐特】
T
Tailwind CSS Blog
V
V2EX
S
SegmentFault 最新的问题
美
美团技术团队
Apple Machine Learning Research
Apple Machine Learning Research
Y
Y Combinator Blog
M
MIT News - Artificial intelligence
量
量子位
aimingoo的专栏
aimingoo的专栏
Stack Overflow Blog
Stack Overflow Blog
Engineering at Meta
Engineering at Meta
GbyAI
GbyAI
P
Proofpoint News Feed
D
DataBreaches.Net
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
A
About on SuperTechFans

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
dpBento: Benchmarking DPUs for Data Processing
Jiasheng Hu, Chihan Cui, Anna Li, Raahil Vora, Yuanfan Chen, Phi · 2025-04-08 · via cs.DB updates on arXiv.org

Data processing units (DPUs, SoC-based SmartNICs) are emerging data center hardware that provide opportunities to address cloud data processing challenges. Their onboard compute, memory, network, and auxiliary storage can be leveraged to offload a variety of data processing tasks. Although recent work shows promising benefits of DPU offloading for specific operations, a comprehensive view of the implications of DPUs for data processing is missing. Benchmarking can help, but existing benchmark tools lack the focus on data processing and are limited to specific DPUs. In this paper, we present dpBento, a benchmark suite that aims to uncover the performance characteristics of different DPU resources and different DPUs, and the performance implications of offloading a wide range of data processing operations and systems to DPUs. It provides an abstraction for automated performance testing and reporting and is easily extensible. We use dpBento to measure recent DPUs, present our benchmarking results, and highlight insights into the potential benefits of DPU offloading for data processing.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。