With the first exascale computing systems in production, tuning and scaling HPC applications to fully utilize the available hardware resources has become more important than ever. Thus, there is a strong need for software tools that assist application developers with this task. The Score-P instrumentation and measurement infrastructure plays a major role in filling this gap. Score-P is a community-driven, highly scalable tool suite for profiling and event tracing of massively parallel HPC application codes, and aimed to be easy to use. It provides measurement data via common data formats and runtime interfaces for a variety of complementary analysis tools developed by multiple institutions and companies, allowing users to gain insights into the communication, synchronization, input/output, and scaling behavior of their applications, pinpointing performance bottlenecks and their causes. In this article, we provide an overview of the current state of the Score-P infrastructure and its related tools ecosystem Cube, Extra-P, TAU, Scalasca, and Vampir. In particular, we detail Score-P's current design and architecture, both of which are highly flexible and extensible. Moreover, we describe how Score-P interacts with the analysis tools mentioned above and highlight the major extensions implemented over the past 10+ years to keep pace with the rapidly changing landscape of HPC hardware and parallel application programming interfaces. Furthermore, we discuss emerging challenges, particularly with respect to the ever-growing heterogeneity in both hardware and software, for collecting and analyzing performance data from applications running on future top-tier computing systems.
Feld et al. (Wed,) studied this question.