Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on “white-box,” that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

中文翻译：

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详细神经元模拟的分析性能建模和分析

大型科学计划正试图通过模拟更大尺度的脑组织和越来越多的生物细节来重建和模拟大脑。并行计算机性能的指数级增长一直在支持这些发展，同时神经科学模拟代码的维护者努力优化和有效地利用新的硬件功能。迄今为止，用于模拟生物网络的最先进软件是使用性能工程实践开发的，但缺乏对计算和性能特征的彻底分析和建模，尤其是在形态学详细的神经元模拟的情况下. 其他计算科学已经成功地使用了分析性能工程，它基于“白盒”，即第一性原理性能模型，以深入了解仿真内核的计算特性，帮助开发人员进行性能优化并最终推动协同设计工作，但据我们所知，基于模型的性能尚未进行神经元模拟的分析。我们详细研究了基于执行-缓存-内存性能模型的形态学详细神经元模拟的共享内存性能。我们证明该模型可以准确预测构成所研究神经元模型的几乎所有内核的运行时间。获得的洞察力用于识别模拟中潜在的性能瓶颈的主要控制机制。讨论了这种分析对神经仿真软件优化和未来硬件架构最终协同设计的影响。从这个意义上说，我们的工作代表了对理解生物网络模拟的性能特性的有价值的概念和定量贡献。