Abstract
Performance analysis is essential for understanding the performance behaviors of large-scale parallel applications on modern supercomputers. Current performance analysis techniques are based on either profiling or tracing. Profiling incurs low costs during runtime but misses important information for identifying underlying bottlenecks, while tracing brings unacceptable overhead at large scales. In this book, we leverage static information, such as program structures and data dependence, from source codes and executable binaries to guide dynamic analysis, which achieves the analyzability of tracing with the overhead of profiling. We apply this approach to many performance analysis tasks, including memory monitoring, communication analysis, scalability analysis, and noise detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
TOP500 website (2020). http://top500.org/
Vetter, J., & Chambreau, C. (2005). mpip: Lightweight, scalable MPI profiling.
Tallent, N. R., Adhianto, L., & Mellor-Crummey, J. M. (2010). Scalable identification of load imbalance in parallel executions using call path profiles. In Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–11). IEEE Computer Society.
Tallent, N. R., et al. (2009). Diagnosing performance bottlenecks in emerging petascale applications. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (pp. 1–11). IEEE.
Intel Trace Analyzer and Collector. https://software.intel.com/en-us/trace-analyzer
Zhai, J., Chen, W., & Zheng, W. (2010). PHANTOM: Predicting performance of parallel applications on large-scale parallel machines using a single node. In PPoPP.
Geimer, M., et al. (2010). The scalasca performance toolset architecture. Concurrency and Computation: Practice and Experience, 22(6), 702–719.
Linford, J. C., et al. (2017). Performance analysis of openSHMEM applications with TAU commander. In Workshop on OpenSHMEM and Related Technologies (pp. 161–179). Springer.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zhai, J., Jin, Y., Chen, W., Zheng, W. (2023). Background and Overview. In: Performance Analysis of Parallel Applications for HPC. Springer, Singapore. https://doi.org/10.1007/978-981-99-4366-1_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-4366-1_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4365-4
Online ISBN: 978-981-99-4366-1
eBook Packages: Computer ScienceComputer Science (R0)