{"ID":2876270,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00883","arxiv_id":"2509.00883","title":"Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.00883","url_pdf":"https://arxiv.org/pdf/2509.00883v1","authors":"[\"Denis Los\",\"Igor Petushkov\"]","published":"2025-08-31T14:51:19Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}