Techniques for Managing Polyhedral Dataflow Graphs
Techniques for Managing Polyhedral Dataflow Graphs
复制标题
管理多面体数据流图的技术
DOI：
10.1007/978-3-030-99372-6_9
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Olschanowsky, Catherine
中科院分区：
文献类型：
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作者：
Shankar, Ravi;Orenstein, Aaron;Rift, Anna;Popoola, Tobi;MacDonald;Yang, Shuai;Mikesell, T. Dylan;Olschanowsky, Catherine
科学应用程序，特别是遗留应用程序，包含丰富的科学知识。随着硬件的变化，应用程序需要移植到新的体系结构，并扩展到包括科学进步。因此，经常会遇到性能瓶颈和死代码等问题。一个可视化的故障流表示可以帮助性能专家识别和调试这些问题。稀疏多面体框架（SPF）的计算API为工具提供了一个单一的入口点来生成和操作多面体拓扑图，并转换应用程序。然而，在查看为科学应用生成的图形时，存在几个障碍。这些图很大，并且很难操纵它们的布局以遵守执行顺序。本文介绍了一个案例研究，使用计算API来表示SPF中的科学应用程序GeoAc。探索生成的多面体仿射图的优化机会，并使用几种图形简化来解决限制，以提高其可用性。
Scientific applications, especially legacy applications, contain a wealth of scientific knowledge. As hardware changes, applications need to be ported to new architectures and extended to include scientific advances. As a result, it is common to encounter problems like performance bottlenecks and dead code. A visual representation of the dataflow can help performance experts identify and debug such problems. The Computation API of the sparse polyhedral framework (SPF) provides a single entry point for tools to generate and manipulate polyhedral dataflow graphs, and transform applications. However, when viewing graphs generated for scientific applications there are several barriers. The graphs are large, and manipulating their layout to respect execution order is difficult. This paper presents a case study that uses the Computation API to represent a scientific application, GeoAc, in the SPF. Generated polyhedral dataflow graphs were explored for optimization opportunities and limitations were addressed using several graph simplifications to improve their usability.