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Attaining high performance with MPI applications requires efficient message matching to minimize message processing overheads and the latency these overheads introduce into application communication. In this paper, we use a validated simulation-based approach to examine the relationship between MPI message matching performance and application time-to-solution. Specifically, we examine how the performance of several important HPC workloads is affected by the time required for matching. Our analysis yields several important contributions: (i) the performance of current workloads is unlikely to be significantly affected by MPI matching unless match queue operations get much slower or match queues get much longer; (ii) match queue designs that provide sublinear performance as a function of queue length are unlikely to yield much benefit unless match queue lengths increase dramatically; and (iii) we provide guidance on how long the mean time per match attempt may be without significantly affecting application performance. The results and analysis in this paper provide valuable guidance on the design and development of MPI message match queues.

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. The results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

The objective of this research is to demonstrate the sensitivity of the hysteretic, nonclassical acoustic nonlinear parameter, α to track changes in the microstructure of 9Cr–1Mo ferritic martensitic steel due to thermal aging. The α parameter is measured with a non-contact nonlinear resonance ultrasound spectroscopy (NRUS) system with an air-coupled source and a laser Doppler vibrometer (LDV) receiver. This NRUS setup is used to track changes in multiple 9Cr–1Mo specimens subjected to different aging times at the same 650 ∘ C temperature. These α results are shown to be highly sensitive to the associated changes in the microstructure of the 9Cr–1Mo specimens, and are then compared to three different parameters – Rockwell hardness, Young's modulus, E and the classical acoustic nonlinear parameter, β – all measured in the same specimens. These results are then combined to infer microstructure changes such as the removal of dislocations and the formation of carbide precipitates occurring in the 9Cr–1Mo specimens during thermal aging.