EAGER: Myriad: a new architecture for parallel multiscale simulation on CPU/GPU

EAGER: Myriad：CPU/GPU 上并行多尺度模拟的新架构

• 批准号: 1743214
• 负责人: Thomas Cleland
• 金额: $ 29.96万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1743214&HistoricalAwards=false
• 关键词: EAGER; Myriad; new; architecture; parallel

The expansion of scientific knowledge is continually improving human understanding of the brain and other biological systems. Increasingly, however, it is becoming clear that many of the capacities of the brain arise from rich and complex interactions among its many regions. Conversely, disorders of the brain and body often arise from subtle deficits that gradually impair the coordinated function of these interacting neural and biological systems until, at last, they begin to collapse and major symptoms are expressed. Because these networks of interactions are too complex to simply intuit, scientists construct computer models so that the work of many different scientific studies can be brought together and gradually constructed into a unified understanding of the intricate systems under study. This leads to broader understandings of our complex brains and bodies that would not be possible without such quantitative models. The goal of the Myriad project is to provide a software platform for biological modeling that makes it much easier to harness the power of modern parallel-processing computer systems. A second goal is to implement a simulator that uses this platform to create detailed models of neurons and neural networks. Myriad is a compartmental simulator platform based on a shared-memory architecture and designed for computational speed based on NVIDIA GPU (CUDA) or parallel CPU execution. Its transformative potential arises from its capacity to automatically parallelize any compartmental model, including those with dense analogue interactions that presently cannot be effectively parallelized, and without requiring the end user to write special, platform-specific parallelization code. Myriad's shared-memory design eschews message-passing, and utilizes a radically granular design approach that flattens hierarchically defined cellular models and can even break up individual isometric compartments by state variable. Specifically, all models that can be represented as isometric, stateful nodes (compartments) connected by any number of arbitrary mechanisms can be simulated with a high degree of parallelism, automatically thread-scaled to the number of available threads and load-balanced with very fine granularity to maximize the utilization of available CPU or GPU cores. Programmatically, end-user models are defined in a Python-based environment and converted into fully-specified C99 code (for CPU or GPU) via code generation techniques that are enhanced by a custom abstract syntax tree (AST) translator and, for NVIDIA GPUs, a custom object specification for CUDA enabling fully on-card execution. The first applications of Myriad will be to simulate biophysically realistic computational models of neurons and networks. Importantly, however, Myriad's generic compartmental solver will be able in principle to parallelize any model framework that can be represented as stateful nodes coupled pairwise by arbitrary mechanisms. Accordingly, to extend Myriad into new scientific areas of study (e.g., gene regulatory networks, epidemiological models, host-virus interactions, ecological systems) will require development only at the higher Python-based software layer, presumably by experts in the relevant field. Finally, as present parallelization strategies are only able to accelerate sparselycoupled models, computational modeling recently has been drawn towards that subset of scientific questions capable of being addressed by these methods. The release of Myriad may reopen the full breadth of quantitative biological questions that can be effectively addressed by parallel simulation. Updates on the Myriad project can be found at http://cplab.net/myriad.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

科学知识的扩展不断提高人类对大脑和其他生物系统的理解。 然而，越来越明显的是，大脑的许多能力源自其许多区域之间丰富而复杂的相互作用。 相反，大脑和身体的疾病往往是由微妙的缺陷引起的，这些缺陷逐渐损害了这些相互作用的神经和生物系统的协调功能，直到最后，它们开始崩溃并表现出主要症状。 由于这些相互作用的网络过于复杂，无法简单地凭直觉来理解，科学家们构建了计算机模型，以便将许多不同科学研究的工作汇集在一起​​，并逐渐构建对所研究的复杂系统的统一理解。 这使得我们能够更广泛地了解我们复杂的大脑和身体，如果没有这样的定量模型，这是不可能的。 Myriad 项目的目标是提供一个用于生物建模的软件平台，使利用现代并行处理计算机系统的能力变得更加容易。 第二个目标是实现一个模拟器，使用该平台创建神经元和神经网络的详细模型。 Myriad 是一个基于共享内存架构的分区模拟器平台，旨在提高基于 NVIDIA GPU (CUDA) 或并行 CPU 执行的计算速度。 其变革潜力源于其自动并行化任何分区模型的能力，包括那些目前无法有效并行化的密集模拟交互模型，并且不需要最终用户编写特殊的、特定于平台的并行化代码。 Myriad 的共享内存设计避开了消息传递，并利用了一种彻底的粒度设计方法，该方法可以扁平化分层定义的细胞模型，甚至可以通过状态变量分解各个等距隔室。 具体来说，所有可以表示为通过任意数量的任意机制连接的等距、有状态节点（部分）的模型都可以以高度并行性进行模拟，自动线程缩放到可用线程的数量，并以非常细的粒度进行负载平衡，以最大限度地提高可用 CPU 或 GPU 核心的利用率。 以编程方式，最终用户模型在基于 Python 的环境中定义，并通过代码生成技术转换为完全指定的 C99 代码（针对 CPU 或 GPU），这些技术通过自定义抽象语法树 (AST) 转换器以及针对 NVIDIA GPU 的 CUDA 自定义对象规范进行增强，从而实现完全卡上执行。 Myriad 的第一个应用将是模拟神经元和网络的生物物理现实计算模型。 然而，重要的是，Myriad 的通用分区求解器原则上能够并行化任何可以表示为通过任意机制成对耦合的有状态节点的模型框架。 因此，要将 Myriad 扩展到新的科学研究领域（例如基因调控网络、流行病学模型、宿主病毒相互作用、生态系统），只需要在基于 Python 的更高软件层进行开发，大概由相关领域的专家进行开发。 最后，由于当前的并行化策略只能加速稀疏耦合模型，因此计算建模最近已被吸引到能够通过这些方法解决的科学问题子集。 Myriad 的发布可能会重新开放定量生物学问题的全部范围，这些问题可以通过并行模拟有效解决。 有关 Myriad 项目的最新信息，请访问 http://cplab.net/myriad。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。