用AI助手自动并行化延迟敏感程序，提升SMT处理器性能17%。
Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors
- 用AI分析代码热点与依赖，生成细粒度并行方案
- 在SMT核心上运行，实现延迟敏感应用17%的平均性能提升
- 适合需要优化低延迟系统的工程师和研究员
延迟敏感型应用因频繁缓存未命中和推测执行失误，在高性能超标量处理器中常导致功能单元利用率低下。由于对单线程性能影响大，这类应用极少使用同时多线程（SMT）技术。本文探索利用SMT支持延迟敏感应用的细粒度并行化。受大语言模型（LLMs）进展启发，我们提出Aira——一个AI驱动的并行化建议工具。通过扩展Cursor IDE中的AI编程代理并接入模型上下文协议，构建端到端的AI代理系统，实现LLM引导的热点检测、动态二进制插桩获取运行时依赖关系、以及面向SMT的性能模拟以预估收益。结合Relic并行框架，我们在真实工业级延迟敏感基准测试上实现了细粒度任务并行，最终获得17%的几何平均性能提升。
原文摘要 · Abstract (English)
Latency-critical applications tend to show low utilization of functional units due to frequent cache misses and mispredictions during speculative execution in high-performance superscalar processors. However, due to significant impact on single-thread performance, Simultaneous Multithreading (SMT) technology is rarely used with heavy threads of latency-critical applications. In this paper, we explore utilization of SMT technology to support fine-grained parallelization of latency-critical applications. Following the advancements in the development of Large Language Models (LLMs), we introduce Aira, an AI-powered Parallelization Adviser. To implement Aira, we extend AI Coding Agent in Cursor IDE with additional tools connected through Model Context Protocol, enabling end-to-end AI Agent for parallelization. Additional connected tools enable LLM-guided hotspot detection, collection of dynamic dependencies with Dynamic Binary Instrumentation, SMT-aware performance simulation to estimate performance gains. We apply Aira with Relic parallel framework for fine-grained task parallelism on SMT cores to parallelize latency-critical benchmarks representing real-world applications used in industry. We show 17% geomean performance gain from parallelization of latency-critical benchmarks using Aira with Relic framework.
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