Abstract
The OpenMP (The OpenMP name is a registered trademark of the OpenMP Architecture Review Board.) application programming interface provides a simple way for programmers to write parallel programs that are portable between machines and vendors. Programmers parallelize their programs to obtain higher performance, but, as the number of cores per processor increases, taking advantage of parallelism efficiently becomes more difficult. To facilitate efficient parallelization and avoid poor utilization of machine resources, programmers need to know where an application is spending time and what factors hinder scalability.
In this paper, we present a Tool for Runtime Instrumentation of OpenMP programs (TRIO) that automatically collects statistics about an application’s use of the OpenMP runtime. TRIO provides statistics such as the total number of times an OpenMP construct is called, the time spent in each OpenMP construct, and the total time spent within the OpenMP runtime. TRIO helps to identify the runtime calls where a program spends most of the time and which constructs are called the most at runtime.
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Notes
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These are application teams and crews and do not refer to OpenMP constructs.
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Acknowledgement
This material is based upon work supported by Subcontract No. B609815 with Argonne National Laboratory and Intel Federal LLC. We thank professor John Mellor-Crummey for his feedback on OMPT and its comparison with TRIO.
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Appendix
Appendix
The TRIO output included here in Fig. 4 is from the CLOMP run mentioned in Sect. 3.2. In the interest of space, we have included only the non-zero fields. The scripts use the “Total” column to process the results. Even though the fork and join barrier times are measured separately, we sum them up for plots. The raw output provides a clearer relationship between OMP_idle and OMP_serial, i.e. Total_OMP_idle can be computed using Total_OMP_serial as, \(((num\_threads) - 1 ) \times Total\_OMP\_serial\).
Raw output from TRIO
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Doodi, T. et al. (2017). OpenMP\(^{\textregistered }\) Runtime Instrumentation for Optimization. In: de Supinski, B., Olivier, S., Terboven, C., Chapman, B., Müller, M. (eds) Scaling OpenMP for Exascale Performance and Portability. IWOMP 2017. Lecture Notes in Computer Science(), vol 10468. Springer, Cham. https://doi.org/10.1007/978-3-319-65578-9_19
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