论文标题
通过交互式数据运动可视化提高性能优化
Boosting Performance Optimization with Interactive Data Movement Visualization
论文作者
论文摘要
在当今的硬件体系结构景观中优化应用程序性能是一项重要但日益复杂的任务，通常需要详细的性能分析。特别是，数据移动和重复使用在优化中起着至关重要的作用，并且在没有详细的程序检查的情况下通常很难改进。性能可视化可以有助于诊断性能问题，但通常依靠通过冗长的程序执行收集的数据。在本文中，我们提出了一个旨在分析数据移动并重复使用以告知有影响力的优化决策的性能可视化，而无需执行程序。我们提出了一种将静态数据流分析与参数化程序仿真相结合的方法，以分析全局数据运动和细粒度数据访问和重用行为，并可视化对程序表示形式的见解。案例研究分析和优化了现实世界的应用程序，证明了我们的工具在指导优化决策方面的有效性，并使性能调整过程更加互动。
Optimizing application performance in today's hardware architecture landscape is an important, but increasingly complex task, often requiring detailed performance analyses. In particular, data movement and reuse play a crucial role in optimization and are often hard to improve without detailed program inspection. Performance visualizations can assist in the diagnosis of performance problems, but generally rely on data gathered through lengthy program executions. In this paper, we present a performance visualization geared towards analyzing data movement and reuse to inform impactful optimization decisions, without requiring program execution. We propose an approach that combines static dataflow analysis with parameterized program simulations to analyze both global data movement and fine-grained data access and reuse behavior, and visualize insights in-situ on the program representation. Case studies analyzing and optimizing real-world applications demonstrate our tool's effectiveness in guiding optimization decisions and making the performance tuning process more interactive.
