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Software-Defined SSDs enable customizable hardware components to effectively optimize storage performance for specific workloads. However, optimizing configurations is challenging due to complex inter-dependencies among numerous parameters. Existing methods are limited by insufficient workload-awareness, high search overhead, and inability to leverage external insights. To address these challenges, LLMs could be a promising technique, as they excel in handling complex, high-dimensional parameter space exploration by leveraging their advanced capability to identify patterns and optimize solutions. In this work, we explore the potential of LLMs in understanding and efficiently managing Software-Defined SSD design space. Specifically, we propose LLM-S3D, an LLM-driven framework that comprehensively understands workloads via a novel compression scheme, efficiently explores configuration spaces, and iteratively optimizes SSD parameters. Evaluation results demonstrate that LLM-S3D delivers a 59.57% performance improvement for target workloads compared to commodity SSDs.

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Exploiting Large Language Models for Software-Defined Solid-State Drives Design

  • 会议论文

  • 发表日期：

    2026年

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  • Qian Wei,
  • Zehao Chen,
  • Tianren Zhou,
  • Wenbin Zhu,
  • Zhenge Jia,
  • Mengying Zhao,
  • Zhaoyan Shen

摘要

Software-Defined SSDs enable customizable hardware components to effectively optimize storage performance for specific workloads. However, optimizing configurations is challenging due to complex inter-dependencies among numerous parameters. Existing methods are limited by insufficient workload-awareness, high search overhead, and inability to leverage external insights. To address these challenges, LLMs could be a promising technique, as they excel in handling complex, high-dimensional parameter space exploration by leveraging their advanced capability to identify patterns and optimize solutions. In this work, we explore the potential of LLMs in understanding and efficiently managing Software-Defined SSD design space. Specifically, we propose LLM-S3D, an LLM-driven framework that comprehensively understands workloads via a novel compression scheme, efficiently explores configuration spaces, and iteratively optimizes SSD parameters. Evaluation results demonstrate that LLM-S3D delivers a 59.57% performance improvement for target workloads compared to commodity SSDs.