Simulations of electrical activity of networks of morphologically detailed neuron models allow for a better understanding of the brain. State-of-the-art simulations describe the dynamics of ionic currents and biochemical processes within branching topological representations of the neurons. Acceleration of such simulation is possible in the weak scaling limit by modeling neurons as indivisible computation units and increasing the computing power. Strong scaling and simulations close to biological time are difficult, yet required, for the study of synaptic plasticity and other use cases requiring simulation of neurons for long periods of time. Current methods rely on parallel Gaussian Elimination, computing triangulation and substitution of many branches simultaneously. Existing limitations are: (a) high heterogeneity of compute time per neuron leads to high computational load imbalance; and (b) difficulty in providing a computation model that fully utilizes the computing resources on distributed multi-core architectures with Single Instruction Multiple Data (SIMD) capabilities. To address these issues, we present a strategy that extracts flow-dependencies between parameters of the ODEs and the algebraic solver of individual neurons. Based on the resulting map of dependencies, we provide three techniques for memory, communication, and computation reorganization that yield a load-balanced distributed asynchronous execution. The new computation model distributes datasets and balances computational workload across a distributed memory space, exposing a tree-based parallelism of neuron topological structure, an embarrassingly parallel execution model of neuron subtrees, and a SIMD acceleration of subtree state updates. The capabilities of our methods are demonstrated on a prototype implementation developed on the core compute kernel of the NEURON scientific application, built on the HPX runtime system for the ParalleX execution model. Our implementation yields an asynchronous distributed and parallel simulation that accelerates single neuron to medium-sized neural networks. Benchmark results display better strong scaling properties, finer-grained parallelism, and lower time to solution compared to the state of the art, on a wide range of distributed multi-core compute architectures.

中文翻译：

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详细神经元模型的异步分支并行仿真

对形态详细的神经元模型网络的电活动进行模拟可以更好地了解大脑。最先进的模拟描述了神经元分支拓扑表示中离子电流和生化过程的动力学。通过将神经元建模为不可分割的计算单元并提高计算能力，可以在弱缩放限制下加速这种模拟。对于突触可塑性的研究和其他需要长时间模拟神经元的用例来说，接近生物时间的强大缩放和模拟是困难的，但也是必需的。当前的方法依赖于并行高斯消除、同时计算三角测量和许多分支的替换。现有的限制是：（a）每个神经元计算时间的高度异质性导致计算负载高度不平衡；(b)难以提供充分利用具有单指令多数据(SIMD)能力的分布式多核架构上的计算资源的计算模型。为了解决这些问题，我们提出了一种提取 ODE 参数与单个神经元代数求解器之间的流依赖性的策略。基于生成的依赖关系图，我们提供了三种用于内存、通信和计算重组的技术，以产生负载平衡的分布式异步执行。新的计算模型在分布式内存空间中分布数据集并平衡计算工作负载，揭示了神经元拓扑结构的基于树的并行性、神经元子树的令人尴尬的并行执行模型以及子树状态更新的SIMD加速。我们的方法的功能在 NEURON 科学应用程序的核心计算内核上开发的原型实现上得到了证明，该原型实现是在 ParalleX 执行模型的 HPX 运行时系统上构建的。我们的实现产生了异步分布式并行模拟，可将单个神经元加速到中等规模的神经网络。与现有技术相比，基准测试结果在各种分布式多核计算架构上显示出更强大的扩展特性、更细粒度的并行性以及更短的解决时间。