Sulis: An EPSRC platform for ensemble computing delivered by HPC Midlands+

Sulis：HPC Midlands 提供的用于集成计算的 EPSRC 平台

• 批准号: EP/T022108/1
• 负责人: David Quigley
• 金额: $ 535.16万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT022108%2F1
• 关键词: Sulis; EPSRC; platform; ensemble; computing

Computer simulation and modelling is increasingly seen as the third pillar of modern science, alongside theory and experiment. Increasingly powerful research computing facilities are required for this activity. Traditionally, the case for these facilities has been made through a scientific need to model larger physical systems, or simulate with increased fidelity. Such calculations benefit from larger and more powerful computers by exploiting ever-larger numbers of computational processing units (cores) within a single calculation. Sulis will support alternative and complementary ways of exploiting parallelism, specifically high throughput computing. Here the focus is on calculations of modest size, i.e. comparable to those which could be executed on a typical high-end workstation PC in a few days, but replicated thousands of times each running with different inputs or model parameters to solve a single problem. Working through this "ensemble" of calculations could easily take decades on a single multi-core PC, or many months with university level facilities. Sulis will allow researchers to complete workflows such as this in less than a week and hence apply their expertise to a broader range of problems and be reactive to availability of new input data.There are many computational tasks which fit into this "ensemble computing" model. One pertinent example is uncertainty quantification (UQ). Rather than simulate a single and likely imperfect model, UQ approaches generate ensembles of possible models and simulates them all. This allows predictions to be made statistically. The most likely outcome of the simulated process can be inferred from the ensemble of outputs, along with a confidence level based on the variability over the outputs. The latter is essential if using simulation as a design or decision making tool. A similar concept may be familiar from weather forecasting - models do not make absolute predictions but instead predict a probability of rain based on the fraction of simulations in which this occurs. This approach is applicable to a range of problems in the physical sciences, such as predicting material properties, yield of chemical processes, the motion of bacteria, fusion plasma stability etc.Other ensemble computing workflows include optimisation problems. Here each of the simulations independently searches a subset of the inputs/parameters for a model, reducing the time taken to locate viable solutions. This is essential, for instance, in studying disordered materials, far closer to the real world than the ideal perfect crystals assumed when seeking only a single solution. Ensemble computing is also used to generate, sample or process large datasets, often for subsequent use as inputs to train modern machine learning algorithms. For this reason Sulis will include a high-capacity multi-petabyte data storage capacity, exploiting modern solid-state storage technologies to reduce bottlenecks arising from reading and writing of data. It will also include a large number of graphics processing units (GPUs) - accelerator devices themselves now ubiquitous for machine-learning applications.A focus on ensemble computing raises challenges to researchers and software engineers. With thousands of simulations, the probability that at least one will fail is substantial. Software must be resilient to this failure. Similarly, managing the input and output of so many calculations can overload traditional data storage subsystems, requiring users to work with database technology rarely encountered by researchers outside of computer science departments. Hence a key feature of the Sulis service will be Research Software Engineering (RSE) support to assist and train users in tackling these problems, future-proofing the competitiveness of UK researchers to the challenges of computing at ever larger scales.

计算机模拟和建模越来越被视为继理论和实验之后现代科学的第三大支柱。这项活动需要越来越强大的研究计算设施。传统上，这些设施的案例是根据对更大的物理系统进行建模或以更高的保真度进行模拟的科学需求而提出的。通过在单次计算中利用越来越多的计算处理单元（核心），此类计算受益于更大、更强大的计算机。 Sulis 将支持利用并行性的替代和补充方式，特别是高吞吐量计算。这里的重点是适度规模的计算，即与可以在几天内在典型的高端工作站 PC 上执行的计算相当，但每次使用不同的输入或模型参数运行复制数千次以解决单个问题。在一台多核 PC 上完成这种“整体”计算很容易需要数十年的时间，而在大学水平的设施上则需要数月的时间。 Sulis 将使研究人员能够在不到一周的时间内完成此类工作流程，从而将他们的专业知识应用于更广泛的问题，并对新输入数据的可用性做出反应。有许多计算任务适合这种“集成计算”模型。一个相关的例子是不确定性量化（UQ）。昆士兰大学的方法不是模拟单个且可能不完美的模型，而是生成可能模型的集合并模拟所有模型。这使得可以进行统计预测。模拟过程最可能的结果可以从输出集合以及基于输出变化的置信水平推断出来。如果使用仿真作为设计或决策工具，后者至关重要。类似的概念在天气预报中可能很常见——模型不会做出绝对的预测，而是根据发生降雨的模拟比例来预测下雨的概率。这种方法适用于物理科学中的一系列问题，例如预测材料特性、化学过程的产量、细菌的运动、聚变等离子体稳定性等。其他集成计算工作流程包括优化问题。这里，每个模拟都会独立搜索模型的输入/参数子集，从而减少找到可行解决方案所需的时间。例如，在研究无序材料时，这一点至关重要，它比仅寻求单一解决方案时假设的理想完美晶体更接近现实世界。集成计算还用于生成、采样或处理大型数据集，通常随后用作训练现代机器学习算法的输入。为此，Sulis将配备大容量的多PB数据存储容量，利用现代固态存储技术来减少数据读写的瓶颈。它还将包括大量图形处理单元（GPU）——加速器设备本身现在在机器学习应用中无处不在。对集成计算的关注给研究人员和软件工程师带来了挑战。经过数千次模拟，至少有一次失败的可能性很大。软件必须能够适应这种故障。同样，管理如此多的计算的输入和输出可能会使传统的数据存储子系统超载，从而要求用户使用计算机科学部门之外的研究人员很少遇到的数据库技术。因此，Sulis 服务的一个关键功能将是研究软件工程 (RSE) 支持，以帮助和培训用户解决这些问题，从而确保英国研究人员在未来应对更大规模计算挑战时的竞争力。