Learned Exascale Computational Imaging (LEXCI)

即将到来的超级计算机将迎来百亿亿次计算的新时代，这将带来机遇和挑战。这种高性能计算（HPC）硬件的原始计算能力有可能彻底改变科学和工业的许多领域。然而，必须开发新的计算算法和软件，以确保新型HPC架构的潜力得以实现。计算成像的目标是从一些观测仪器获得的原始数据中恢复感兴趣的图像，是科学和工业中最广泛遇到的问题之一，在天文学、医学、行星和气候科学、计算机图形学和虚拟现实、地球物理学、分子生物学等领域都有无数的应用。百亿亿次计算的兴起，加上最近仪器仪表的进步，正在产生新的、通常是巨大的数据集，原则上，这些数据集可以首次以高保真度的可解释方式进行成像。然而，为了解锁可解释的、高保真的大数据成像，需要新颖的方法方法、算法和软件实现——我们将开发这些组件，作为学习EXascale计算成像（LEXCI）项目的一部分。首先，传统的计算成像算法基于相对简单的手工图像先验模型，而在LEXCI中，我们将从数据中学习适当的图像先验和物理仪器仿真模型，从而获得更准确的表示。我们的混合技术将以基于模型的方法为指导，以确保有效性、效率、通用性和不确定性量化。其次，我们将开发支持高度并行化和分布式实现的新颖算法结构，以便在广泛的现代HPC架构中部署。第三，我们将在专业的研究软件中实现这些算法。我们的算法结构不仅允许计算分布在多节点架构上，而且还允许内存和存储需求。我们将开发一种分层并行化方法，既针对大规模分布式内存并行化（用于跨处理器和协处理器分发工作），也针对通过向量化或轻量级线程实现轻量级数据并行化（用于在处理器和协处理器上分发工作）。我们的分层并行化方法将确保该软件可以在各种现代HPC系统中使用。结合起来，这些发展将提供一个未来的计算范式，帮助迎来百亿亿次计算成像时代。由此产生的计算成像框架将具有广泛的应用，并将作为项目的一部分应用于许多不同的问题，包括无线电干涉成像、磁共振成像、地震成像、计算机图形学等。最终的软件将部署在最新的高性能计算资源上，以评估其性能，并向社会反馈所获得的计算经验和开发的技术，从而支持百亿亿次计算的全面发展。

The emerging era of exascale computing that will be ushered in by the forthcoming generation of supercomputers will provide both opportunities and challenges. The raw compute power of such high performance computing (HPC) hardware has the potential to revolutionize many areas of science and industry. However, novel computing algorithms and software must be developed to ensure the potential of novel HPC architectures is realized. Computational imaging, where the goal is to recover images of interest from raw data acquired by some observational instrument, is one of the most widely encountered class of problem in science and industry, with myriad applications across astronomy, medicine, planetary and climate science, computer graphics and virtual reality, geophysics, molecular biology, and beyond.The rise of exascale computing, coupled with recent advances in instrumentation, is leading to novel and often huge datasets that, in principle, could be imaged for the first time in an interpretable manner at high fidelity. However, to unlock interpretable, high-fidelity imaging of big-data novel methodological approaches, algorithms and software implementations are required -- we will develop precisely these components as part of the Learned EXascale Computational Imaging (LEXCI) project.Firstly, whereas traditional computational imaging algorithms are based on relatively simple hand-crafted prior models of images, in LEXCI we will learn appropriate image priors and physical instrument simulation models from data, leading to much more accurate representations. Our hybrid techniques will be guided by model-based approaches to ensure effectiveness, efficiency, generalizability and uncertainty quantification. Secondly, we will develop novel algorithmic structures that support highly parallelized and distributed implementations, for deployment across a wide range of modern HPC architectures. Thirdly, we will implement these algorithms in professional research software. The structure of our algorithms will not only allow computations to be distributed across multi-node architectures, but memory and storage requirements also. We will develop a tiered parallelization approach targeting both large-scale distributed-memory parallelization, for distributing work across processors and co-processors, and light-weight data parallelism through vectorization or light-weight threads, for distributing work on processors and co-processors. Our tiered parallelization approach will ensure the software can be used across the full range of modern HPC systems. Combined, these developments will provide a future computing paradigm to help usher in the era of exascale computational imaging.The resulting computational imaging framework will have widespread application and will be applied to a number of diverse problems as part of the project, including radio interferometric imaging, magnetic resonance imaging, seismic imaging, computer graphics, and beyond. The resulting software will be deployed on the latest HPC computing resources to evaluate their performance and to feed back to the community the computing lessons learned and techniques developed, so as to support the general advance of exascale computing.