compute optimization

JD Tech

May 17, 2024 · Artificial Intelligence

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

The article details how JD's advertising retrieval platform tackles the core challenge of balancing limited compute resources with massive data by optimizing compute allocation, improving model scoring efficiency, and enhancing iteration speed through distributed execution graphs, adaptive algorithms, and platform‑level infrastructure improvements.

ANNDistributed Systemsadvertising

0 likes · 24 min read

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

JD Retail Technology

Apr 24, 2024 · Backend Development

Design and Optimization of JD Advertising Retrieval Platform: Adaptive Compute Allocation, High‑Efficiency Search Engine, and Platform‑Scale Infrastructure

The article presents a comprehensive overview of JD's advertising retrieval platform, detailing how it balances limited compute resources with massive data through adaptive compute allocation, distributed execution graphs, elastic systems, and multi‑stage algorithmic improvements to achieve high‑performance, scalable ad matching.

Distributed SystemsJD.comadvertising

0 likes · 22 min read

Design and Optimization of JD Advertising Retrieval Platform: Adaptive Compute Allocation, High‑Efficiency Search Engine, and Platform‑Scale Infrastructure

DataFunTalk

Nov 21, 2023 · Artificial Intelligence

Improving Efficiency of Large-Scale Distributed Training for Large Language Models

Recent advances in large language models have dramatically increased model size and training data, leading to soaring computational costs; this article examines the scaling trends, hardware utilization challenges, distributed training techniques, and ethical considerations, highlighting methods to improve efficiency, reduce costs, and mitigate environmental impact.

AI ethicsLarge Language Modelscompute optimization

0 likes · 29 min read

Improving Efficiency of Large-Scale Distributed Training for Large Language Models