Computational modeling and simulation have become essential tools in the quest to better understand the brain’s makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the brain has led to the development of a large variety of model abstractions and specialized tools, often times requiring high performance computing resources for their timely execution. What has been missing so far was an in-depth analysis of the complexity of the computational kernels, hindering a systematic approach to identifying bottlenecks of algorithms and hardware. If whole brain models are to be achieved on emerging computer generations, models and simulation engines will have to be carefully co-designed for the intrinsic hardware tradeoffs. For the first time, we present a systematic exploration based on analytic performance modeling. We base our analysis on three in silico models, chosen as representative examples of the most widely employed modeling abstractions: current-based point neurons, conductance-based point neurons and conductance-based detailed neurons. We identify that the synaptic modeling formalism, i.e. current or conductance-based representation, and not the level of morphological detail, is the most significant factor in determining the properties of memory bandwidth saturation and shared-memory scaling of in silico models. Even though general purpose computing has, until now, largely been able to deliver high performance, we find that for all types of abstractions, network latency and memory bandwidth will become severe bottlenecks as the number of neurons to be simulated grows. By adapting and extending a performance modeling approach, we deliver a first characterization of the performance landscape of brain tissue simulations, allowing us to pinpoint current bottlenecks for state-of-the-art in silico models, and make projections for future hardware and software requirements.

中文翻译：

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通过分析性能模型了解细胞级脑组织模拟的计算成本。


计算建模和模拟已成为更好地了解大脑的构成并破译其各组成部分的因果关系的重要工具。大脑中生物化学和生物物理过程和结构的广度导致了各种各样的模型抽象和专用工具的开发，通常需要高性能计算资源才能及时执行。到目前为止，缺少的是对计算内核复杂性的深入分析，阻碍了识别算法和硬件瓶颈的系统方法。如果要在新兴计算机上实现全脑模型，则必须仔细共同设计模型和模拟引擎，以实现内在的硬件权衡。我们首次提出基于分析性能建模的系统探索。我们的分析基于三个计算机模型，被选为最广泛使用的建模抽象的代表性示例：基于电流的点神经元、基于电导的点神经元和基于电导的详细神经元。我们发现，突触建模形式，即基于电流或电导的表示，而不是形态细节的水平，是确定计算机模型的内存带宽饱和和共享内存缩放特性的最重要因素。尽管到目前为止，通用计算在很大程度上能够提供高性能，但我们发现，对于所有类型的抽象，随着要模拟的神经元数量的增长，网络延迟和内存带宽将成为严重的瓶颈。 通过调整和扩展性能建模方法，我们首次描述了脑组织模拟的性能景观，使我们能够查明最先进的计算机模型的当前瓶颈，并对未来的硬件和软件需求进行预测。