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1. An Empirical Study of High Performance Computing (HPC) Performance Bugs

   https://doi.org/10.1109/MSR59073.2023.00037  

   Kalam Azad, Md Abul ; Iqbal, Nafees ; Hassan, Foyzul ; Roy, Probir ( May 2023 , IEEE)
2. Taming Performance Variability

   Maricq, A ; Duplyakin, D ; Jimenez, I ; Maltzahn, C ; Stutsman, R ; Ricci, R ( October 2018 , Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18))

   The performance of compute hardware varies: software run repeatedly on the same server (or a different server with supposedly identical parts) can produce performance results that differ with each execution. This variation has important effects on the reproducibility of systems research and ability to quantitatively compare the performance of different systems. It also has implications for commercial computing, where agreements are often made conditioned on meeting specific performance targets. Over a period of 10 months, we conducted a large-scale study capturing nearly 900,000 data points from 835 servers. We examine this data from two perspectives: that of a service provider wishing to offer a consistent environment, and that of a systems researcher who must understand how variability impacts experimental results. From this examination, we draw a number of lessons about the types and magnitudes of performance variability and the effects on confidence in experiment results. We also create a statistical model that can be used to understand how representative an individual server is of the general population. The full dataset and our analysis tools are publicly available, and we have built a system to interactively explore the data and make recommendations for experiment parameters based on statistical analysis of historical data. 

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3. Measuring Network Latency Variation Impacts to High Performance Computing Application Performance

   https://doi.org/10.1145/3184407.3184427  

   Underwood, Robert ; Anderson, Jason ; Apon, Amy ( April 2018 , ICPE '18 Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering)

   In this paper, we study the impacts of latency variation versus latency mean on application runtime, library performance, and packet delivery. Our contributions include the design and implementation of a network latency injector that is suitable for most QLogic and Mellanox InfiniBand cards. We fit statistical distributions of latency mean and variation to varying levels of network contention for a range of parallel application workloads. We use the statistical distributions to characterize the latency variation impacts to application degradation. The level of application degradation caused by variation in network latency depends on application characteristics, and can be significant. Observed degradation varies from no degradation for applications without communicating processes to 3.5 times slower for communication-intensive parallel applications. We support our results with statistical analysis of our experimental observations. For communication-intensive high performance computing applications, we show statistically significant evidence that changes in performance are more highly correlated with changes of variation in network latency than with changes of mean network latency alone. 

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4. Performing up to par? Performance pressure increases undergraduates’ cognitive performance and effort.

   https://doi.org/10.1037/mac0000023  

   Mesghina, Almaz ; Au Yeung, Natalie ; Richland, Lindsey Engle ( April 2022 , Journal of Applied Research in Memory and Cognition)
5. Genes, Morphology, Performance, and Fitness: Quantifying Organismal Performance to Understand Adaptive Evolution

   https://doi.org/10.1093/icb/icad096  

   Matthews, David_G ; Dial, Terry_R ; Lauder, George_V ( July 2023 , Integrative And Comparative Biology)

   To understand the complexities of morphological evolution, we must understand the relationships between genes, morphology, performance, and fitness in complex traits. Genomicists have made tremendous progress in finding the genetic basis of many phenotypes, including a myriad of morphological characters. Similarly, field biologists have greatly advanced our understanding of the relationship between performance and fitness in natural populations. However, the connection from morphology to performance has primarily been studied at the interspecific level, meaning that in most cases we lack a mechanistic understanding of how evolutionarily relevant variation among individuals affects organismal performance. Therefore, functional morphologists need methods that will allow for the analysis of fine-grained intraspecific variation in order to close the path from genes to fitness. We suggest three methodological areas that we believe are well suited for this research program and provide examples of how each can be applied within fish model systems to build our understanding of microevolutionary processes. Specifically, we believe that structural equation modeling, biological robotics, and simultaneous multi-modal functional data acquisition will open up fruitful collaborations among biomechanists, evolutionary biologists, and field biologists. It is only through the combined efforts of all three fields that we will understand the connection between evolution (acting at the level of genes) and natural selection (acting on fitness).

    

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