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Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes–Hut algorithm. They demonstrate good scalability and prove a runtime complexity of O (n log 2 ⁡ n). In this comment paper, we show that with careful consideration of the algorithm and a rigorous proof, the theoretical runtime can even be classified as O (n log ⁡ n). • Improved serial runtime bound for the adapted Barnes–Hut algorithm to Θ(n*log(n)). • Improved the parallel runtime bound to Θ(n/p*log(n)+p). • Mathematical justification that the given runtime bound is sharp. [ABSTRACT FROM AUTHOR]

Performance models are powerful instruments for understanding the performance of parallel systems and uncovering their bottlenecks. Already during system design, performance models can help ponder alternative development options. However, creating a performance model – whether theoretically or empirically – for an entire application that does not exist yet is challenging. In this paper, we propose to generate performance models of full programs from performance models of their components using formal composition operators derived from parallel design patterns. As long as the design of the overall system follows such a pattern, its performance model can be predicted with reasonable accuracy without an actual implementation. We demonstrate our approach with design patterns of varying complexity, including pipeline, task pool, and eventually MapReduce, which is representative of a broad class of data-analytics applications. • Parallel design patterns are used to generate composable performance models. • Thorough benchmarking of parallel design patterns on multiple compute nodes. • Real-application use case of MapReduce with Hadoop. • Uniform theoretical treatment of parallel design patterns and their performance. [ABSTRACT FROM AUTHOR]