关于超级计算发展战略方向的思考

Thinkings on Development Strategy of Supercomputing

        葛蔚(中国科学院过程工程研究所 北京 100190)
        郭力(中国科学院过程工程研究所 北京 100190)
        李静海(中国科学院过程工程研究所 北京 100190)
        陈左宁(国家并行计算机工程技术研究中心 北京 100190)
        胡苏太(国家并行计算机工程技术研究中心 北京 100190)
        刘鑫(国家并行计算机工程技术研究中心 北京 100190)

         超级计算;多尺度计算模式;能量最小多尺度(EMMS)范式;虚拟过程工程

        supercomputing;multi-scale model;EMMS Paradigm;virtual process engineering

        超级计算能力是国家科技竞争力乃至综合国力的重要标志,也是经济社会发展、国防和国家安全的重要支撑。特别是在网络化、大数据、云计算和虚拟现实迅猛发展的背景下,超级计算不但是科技发展的利器,正引发科研模式的根本变革,也将成为重要的社会基础设施,并最终深刻地改变我们的生产和生活方式。但目前超级计算正面临应用效率低、能耗高、稳定性差和难以应用等严峻挑战。基于在复杂系统多尺度模拟方面多年的探索和积累,文章提出,按照计算对象、模型、软件与硬件的逻辑和结构一致的原理优化计算机体系结构,缓解和克服商品化通用硬件系统开发中的难点,可能是突破这些瓶颈的一条具有普遍意义的途径。

        Supercomputing power has now become an important index of science and technology competitiveness of a country, and also a great support to the economic and social development and national security. Moreover, with the drastic development of Internet, big data, cloud computing, and virtual reality technologies, supercomputing will bring about profound changes to production and life styles of modern societies. Nevertheless, contemporary supercomputing is facing grand challenges in terms of low efficiency, high power consumption, poor robustness, and difficulties in applications. General-purpose supercomputing based on commercial processors is not likely to solve the problems completely. Based on our explorations in multiscale simulation of complex systems, we suggest that keeping the structural and logical consistency between the computed system, model, software, and hardware is an effective way towards high efficiency and scalability of supercomputing in the future. In fact, multiscale structure is ubiquitous in natural and industrial systems, which is, after all, a reflection of the hierarchical nature of the physical world and is, therefore, of general significance to a wider range of applications. For classical mechanical and thermal processes, the computational complexity usually decreases with decreasing scales, while the computational cost increases exponentially. Fortunately, with reasonable coarse-graining based on meso-scale models, this cost can be reduced by several orders by developing additive discrete methods which can achieve good accuracy, efficiency, and scalability for supercomputing. On the other hand, computation at large scales can be accelerated by introducing stability constraints to the dynamics models. Multiscale computer hardware conforming to this software framework is expected to have high efficiency and speeds up the computation even further. We have already explored the feasibility of this multi-scale strategy for model, software, and hardware developments in different applications, and have achieved very encouraging results. We suggest that this strategy should be given enough considerations in the development of future supercomputers.