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GaiaDB 4.0 introduces a zero‑ETL parallel query engine that leverages multi‑core CPUs to run OLTP and OLAP workloads on a single cluster, dramatically improving complex query performance while offering configurable parallelism and a rich set of parallel operators.
This article explains how Baidu Cloud evolved its physical network fault‑location workflow—from manual analysis and integrated multi‑signal algorithms to AI‑driven reasoning with large language models—highlighting structured prompting, multi‑agent collaboration, and measurable improvements in accuracy and automation.
This article examines the impact of GPU communication bottlenecks on large‑model training, analyzes hash‑collision issues in high‑performance networks, and presents three practical solutions—including increasing RDMA streams, affinity‑aware scheduling, and dynamic load balancing—to boost effective network bandwidth up to 95%.
This article presents a detailed technical overview of accelerating Spark's compute engine using ClickHouse as a native backend, covering Spark performance background, ClickHouse's advantages, the design and implementation of a Spark‑Native acceleration solution, and extensive performance evaluation results.
This article reviews the evolution of databases and large models, explains the role of vector databases and Retrieval‑Augmented Generation (RAG) in AI‑enhanced data management, and showcases Baidu Cloud's VectorDB and DBSC solutions for intelligent database operations and knowledge‑driven services.
This article walks through the complete workflow for creating an AI‑native conversational 24‑point game using Baidu Cloud Qianfan AppBuilder, covering methodology, component design, agent architecture, and implementation details with code examples and visual diagrams.
This article explains how to design, develop, preview, debug, and publish NPM component extension packages for the Baidu AI‑Speed platform, covering supported tech stacks, extension mechanisms, core modules, development tools, code conversion examples, and common deployment questions.
Using a martial‑arts analogy, the article explains why training massive AI models now requires thousands of GPUs or mixed‑chip clusters, outlines three key steps—inter‑connect, distributed parallel strategies, and accelerator acceleration—and shows how Baidu’s Baige platform achieves near‑full efficiency across GPU, Kunlun and Ascend chips.
An exclusive interview with Baidu’s senior database architects reveals the motivations behind building a dedicated enterprise vector database, details its novel column‑store engine, C++‑based retrieval stack, performance gains over open‑source solutions, multi‑modal support, RAG integration, and future research directions.
The article explains the scaling challenges of ever‑larger LLMs, introduces the MFU performance metric, surveys industry parallelism and memory‑saving techniques, and details Baidu’s AIAK‑LLM suite—including resource, component and acceleration layers—as well as concrete training and inference optimizations that raise MFU by 30‑60% and cut deployment costs.
