ScALPEL：一个用于应用程序性能监控的可扩展自适应轻量级性能评估库
ScALPEL: A Scalable Adaptive Lightweight Performance Evaluation Library for application performance monitoring
摘要：
随着超级计算机在规模和能力上的持续增长，隔离性能下降的处理器和系统级原因变得越来越困难。在过去几年中，已经建立/提出了大量的性能分析和监测工具。然而，这些工具有几个重要的缺点，特别是在分布式环境中。在本文中，我们提出了ScALPEL，一个可扩展的自适应轻量级性能评估库的应用程序的性能监测功能级别。我们的方法提供了几个明显的优势。首先，ScALPEL可在各种架构中移植，并且其选择性地监视功能的能力表现出低运行时开销，使其能够用于大规模生产应用程序。其次，它是运行时可配置的，支持动态选择要分析的函数以及每个函数的感兴趣的事件。第三，我们的方法是透明的，因为它不需要修改源代码。最后，ScALPEL实现为一个可插拔的单元，通过重用现有的性能监控框架，如Perfmon和PAPI，并扩展它们，以支持顺序和MPI应用程序。
英文摘要：
As supercomputers continue to grow in scale and capabilities, it is becoming increasingly difficult to isolate processor and system level causes of performance degradation. Over the last several years, a significant number of performance analysis and monitoring tools have been built/proposed. However, these tools suffer from several important shortcomings, particularly in distributed environments. In this paper we present ScALPEL, a Scalable Adaptive Lightweight Performance Evaluation Library for application performance monitoring at the functional level. Our approach provides several distinct advantages. First, ScALPEL is portable across a wide variety of architectures, and its ability to selectively monitor functions presents low run-time overhead, enabling its use for large-scale production applications. Second, it is run-time configurable, enabling both dynamic selection of functions to profile as well as events of interest on a per function basis. Third, our approach is transparent in that it requires no source code modifications. Finally, ScALPEL is implemented as a pluggable unit by reusing existing performance monitoring frameworks such as Perfmon and PAPI and extending them to support both sequential and MPI applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
