OpenPME: Open Particle Mesh Environment for Systems Biology

并行高性能计算（HPC）对于研究复杂现实世界现象的科学模型，准确预测其行为和对扰动的反应以及分析现实世界实验的大型数据集至关重要。这种特别具有挑战性的应用来自系统生物学领域，其中模拟细胞和细胞系统的复杂模型，以逆向工程生物机制。建模和仿真的过程是由高分辨率的3D显微图像驱动的，这些图像也需要进行真实的实时处理。因此，建模、仿真和数据分析关键取决于HPC平台的有效使用。然而，开发在并行HPC硬件上高效运行的强大且通用的数值方法是一项具有挑战性、容易出错且耗时的任务。开发人员需要了解手头的问题域、数学模型以及算法规范对底层系统架构和性能的影响。很少，所有这些复杂性可以在一个给定的项目中处理，因此定义的应用程序和HPC系统之间的知识差距。在这个项目中，我们缩小了高层次的问题描述和最终的HPC代码之间的差距通过引入透明的抽象层，包括：一个特定领域的语言，编译器，自适应运行时系统，和开发环境。我们专注于通用粒子网格方法作为数值模拟框架，因为它们可以模拟离散和连续模型并处理图像，因此涵盖了系统生物学的整个工作流程。我们将创建粒子网格算法的第一个语义描述，并从中派生出一个正式的特定于领域的语言。围绕这个语言，我们将设计一个工具流和集成开发环境，提供即时的用户反馈和交互式调试支持。我们的运行时系统将基于目前正在开发的粒子网格方法的OpenFPM库。利用领域知识和运行时监控，我们将在语言级别以及运行时开发静态和动态优化。为了评估我们的语言和优化，三个来自计算生物学的驱动程序应用程序将在运行时性能，可扩展性和开发人员的生产力方面实现和评估。

Parallel high-performance computing (HPC) is essential to study scientific models of complex real-world phenomena, to accurately predict their behavior and reaction to perturbations, and analyze large data sets of real-world experiments. Particularly challenging applications of this kind arise from the field of systems biology,where complex models of cells and systems of cells are simulated in order to reverse-engineer biological mechanisms. The process of modeling and simulation is driven by high-resolution 3D microscopy images, which also need to be processed in real time. Modeling, simulation, and data analysis hence critically depend on effective use of HPC platforms. Developing robust and versatile numerical methods that run efficiently on parallel HPC hardware, however, is a challenging, error-prone, and time-consuming task. Developers need to understand the problem domain at hand, the mathematical models, as well as the impact of algorithmic specifications on the underlying system architecture and performance. Rarely, all of these intricacies can be dealt with in a given project, hence defining a knowledge gap between the application and the HPC system.In this project, we narrow the gap between a high-level problem description and the final HPC code by introducing transparent layers of abstraction, consisting of: a domain-specific language, a compiler, an adaptive runtime system, and a development environment. We focus on generic particle-mesh methods as the numerical simulation framework, because they can simulate both discrete and continuous models and process images, hence covering the entire workflow of systems biology. We will create the first semantic description of particle-mesh algorithms and derive a formal domain-specific language from it. Around this language, we will design a tool-flow and integrated development environment that provides instant user feedback and support for interactive debugging.Our runtime system will be based on the OpenFPM library for particle-mesh methods being currently developed. Leveraging domain knowledge and runtime monitoring, we will develop static and dynamic optimizations at the language level, as well as at runtime. To asses our language and optimizations, three driver applications from computational biology that pose outstanding problems will be implemented and evaluated in terms of runtime performance, scalability, and developers productivity.