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.
翻译:优化当今硬件架构景观中的应用性能是一项重要但日益复杂的任务,往往需要详细的业绩分析。 特别是,数据流动和再利用在优化方面发挥着关键作用,而且往往难以在不进行详细程序检查的情况下加以改进。 性能可视化可以帮助诊断性能问题,但一般依赖通过冗长的方案执行而收集的数据。 在本文件中,我们介绍了一种性能可视化,旨在分析数据流动和再利用,以便为影响性能优化决策提供信息,而无需执行方案。 我们提出了一种方法,将静态数据流分析与参数化程序模拟结合起来,以分析全球数据流动和精细采集的数据访问和再利用行为,并直观地了解关于方案代表性的见解。 案例研究分析和优化现实世界应用展示了我们工具在指导优化决策和使性能调整过程更具互动性方面的有效性。