Summary of Tackling the Dynamicity in a Production Llm Serving System with Sota Optimizations Via Hybrid Prefill/decode/verify Scheduling on Efficient Meta-kernels, by Mingcong Song et al.
Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels
by Mingcong Song, Xinru Tang, Fengfan Hou, Jing Li, Wei Wei, Yipeng Ma, Runqiu Xiao, Hongjie Si, Dingcheng Jiang, Shouyi Yin, Yang Hu, Guoping Long
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed XY-Serve system is an end-to-end production large language model serving system designed for low-latency and cost-efficient LLM processing on AI accelerators like DSAs with tile-based programming models. The system addresses the challenges of workload variability caused by dynamic input-output lengths and advanced optimization techniques, achieving up to 89% end-to-end throughput improvement compared to current baselines on Ascend NPUs. The approach also outperforms existing GEMM (14.6%) and attention (21.5%) kernels relative to existing libraries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary XY-Serve is a new way to make large language models work better and faster for things like text processing. It’s designed for special computers called AI accelerators, which are really good at doing lots of calculations quickly. The problem was that these computers had trouble with the language models because they didn’t know how big or small the input would be. XY-Serve solves this by breaking down the calculations into smaller parts that the computer can handle better. It also makes some calculations faster and more efficient, which helps make it work even better. |
Keywords
» Artificial intelligence » Attention » Large language model » Optimization
