性能报告和天气和气候矮在GPU、MIC和Optalysys光学处理器上的优化实现
Performance report and optimized implementation of Weather & Climate Dwarfs on GPU, MIC and Optalysys Optical Processor
摘要：
本文档是为ESCAPE项目创建的可交付成果报告之一。ESCAPE是Energy-efficient Scalable Algorithms for Weather Prediction at Exascale的缩写。该项目为欧洲业务数值天气预报和未来气候模型开发世界级的极端规模计算能力。这是通过识别天气和气候侏儒来完成的，这些侏儒是计算和通信方面的关键模式（本着伯克利侏儒的精神）。然后针对不同的硬件架构（单节点和多节点）优化这些侏儒，并探索替代算法。性能可移植性是通过使用领域特定的语言来解决的。在这里，我们总结了在CPU、Xeon Phi、GPU和Optalysys光学处理器上对侏儒进行优化的工作。我们限制自己的一个子集的侏儒配置和问题的大小小到足以在一个节点上执行。此外，我们使用的时间解决方案作为主要的性能指标。侏儒的多节点优化和特定于能量的优化超出了本报告的范围，将在可扩展的D3.4中进行描述。为了涵盖重要的算法主题，我们选择了与动力学核心以及柱物理相关的矮星。具体来说，我们专注于制定相关的频谱代码，如ECMWF的IFS代码。该报告的主要发现是：（a）使用编译器指令在基于CPU的系统上加速1.1 - 2.5倍的矮星，（b）使用数据局部优化在GPU上加速矮星的数量级（光谱变换为23倍，MPDATA为9倍）和（c）以纯粹的光学方式证明光谱变换的可行性。
英文摘要：
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying Weather & Climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. Here we summarize the work performed on optimizations of the dwarfs on CPUs, Xeon Phi, GPUs and on the Optalysys optical processor. We limit ourselves to a subset of the dwarf configurations and to problem sizes small enough to execute on a single node. Also, we use time-to-solution as the main performance metric. Multi-node optimizations of the dwarfs and energy-specific optimizations are beyond the scope of this report and will be described in Deliverable D3.4. To cover the important algorithmic motifs we picked dwarfs related to the dynamical core as well as column physics. Specifically, we focused on the formulation relevant to spectral codes like ECMWF's IFS code. The main findings of this report are: (a) Acceleration of 1.1x - 2.5x of the dwarfs on CPU based systems using compiler directives, (b) order of magnitude acceleration of the dwarfs on GPUs (23x for spectral transform, 9x for MPDATA) using data locality optimizations and (c) demonstrated feasibility of a spectral transform in a purely optical fashion.
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