Principles and Methodologies for Serial Performance Optimization

Sujin Park, Mingyu Guan, Xiang Cheng, and Taesoo Kim, Georgia Institute of Technology

Throughout the history of computer science, optimizing existing systems to achieve higher performance has been a longstanding aspiration. While the primary emphasis of this endeavor lies in reducing latency and increasing throughput, these two are closely intertwined, and answering the how question has remained a challenge, often relying on intuition and experience.

This paper introduces a systematic approach to optimizing sequential tasks, which are fundamental for overall performance. We define three principles—task removal, replacement, and reordering—and distill them into eight actionable methodologies: batching, caching, precomputing, deferring, relaxation, contextualization, hardware specialization, and layering. Our review of OSDI and SOSP papers over the past decade shows that these techniques, when taken together, comprehensively account for the observed sequential optimization strategies.

To illustrate the framework’s practical value, we present two case studies: one on file and storage systems, and another analyzing kernel synchronization to uncover missed optimization opportunities. Furthermore, we introduce SysGPT, a fine-tuned GPT model trained on curated literature analysis, which offer context-aware performance suggestions. SysGPT’s outputs are more specific and feasible than GPT-4’s, aligning with core strategies from recent research without direct exposure, demonstrating its utility as an optimization assistant.