Realistic Simulations of Photoactive Systems on HPC Clusters with Many-Core Processors

这个跨学科项目旨在通过在下一代处理器平台上精确但计算要求高的模拟来解决理解光活性分子系统中能量转换的原子机制方面的挑战。最近对光合复合体的实验表明，在较大的叶绿素网络中，部分相干机制支持从天线到反应中心的单向能量传递。为了阐明潜在的物理机制，并可能将其应用于有机太阳能电池等高效设备的设计，需要基于原子第一原理计算的精确模拟。虽然模拟硫细菌中最原始的光合复合体的时间分辨光谱可以用“运动层次方程”（HEOM）在单个图形处理器上进行，但更大的光活性分子系统需要具有多核处理器的高性能集群。这需要新的策略来优化和扩展应用程序，以回答有关激子的时间尺度和选择途径的基本问题。由于进一步提高单核性能的潜力有限，大规模并行多核架构正日益成为高性能计算集群的主力。从计算机科学的角度来看，模块化HEOM代码是研究利用当前和即将到来的多核系统固有并行性的一般适用方法的完美候选者。该项目将确定跨异构平台的通用优化策略，并将使用图理论方法为共享和分布式内存布局提供避免通信的设计原则。

This interdisciplinary project aims to tackle challenges in the understanding of atomistic mechanisms of energy conversion in photoactive molecular systems through accurate but computationally demanding simulations on next generation processor platforms. Recent experiments on photosynthetic complexes suggest a partly coherent mechanism supporting unidirectional energy-transfer from the antenna to the reaction center in larger chlorophyll networks. To elucidate the underlying physical mechanism and to possibly apply it to the design of efficient devices such as organic solar cells, requires accurate simulations based on atomistic first-principle calculations.While the simulation of time-resolved spectra for one of the most primitive photosynthetic complexes found in sulfur bacteria can be performed on single graphics processors with the "Hierarchical Equations Of Motion" (HEOM),larger photoactive molecular systems require high-performance clusters with many-core processors. This demands for new strategies for optimizing and scaling the application to answer fundamental questions about the time-scale and chosen pathway of the excitons. Because of the limited potential of further improving the single-core performance, massively parallel many-core architectures are increasingly becoming the work-horse of high-performance compute clusters. From the computer science perspective, the modular HEOM code is a perfect candidate for studying generally applicable approaches to exploit the inherent parallelism of current and forthcoming many-core systems. The project will identify generic optimization strategies across heterogeneous platforms, and will contribute communication-avoiding design principles for shared and distributed memory layouts using graph theoretical methods.