RSE training in algorithms for exascale simulations

英国的百亿亿次计算领域正处于一个激动人心的阶段，资金被分配到新的架构、新的软件框架和创新的算法上。通过培训rse，我们有机会将这些领域的进展嵌入到学术研究和工业应用的核心，使英国成为百亿亿次模拟的国际领导者。为了抓住这个机会，rse必须接受算法培训，以便他们能够积极参与这一领域的研究。为了在编写和优化软件时做出明智和创造性的设计选择，rse需要具备算法的核心知识，这样他们才能自信地进行创新，避免学术界和工业合作伙伴通过研究和经验已经意识到的常见陷阱。如果这些核心知识不能通过torse传递并在整个RSE社区共享，那么通过ExCALIBUR计划研究项目取得的进展就有可能无法在学术和工业应用中产生重要影响。正如RSE知识集成景观回顾第7.3节所述，RSE有潜力积极参与研究是至关重要的。这是这项工作对熟练的研究生的主要吸引力之一，也是留住熟练的rse的关键。《景观评论》承认，新算法的设计本身就是一个研究领域，需要“强大的领域特定知识”。我们建议提供这方面的培训，以及学术研究人员、研究生、研究人员和工业合作伙伴之间的知识交流和网络机会。我们建议举办两个为期三天的研讨会和一个暑期学校，以提供最先进的算法和基础数学和数值分析的核心知识的培训。在这些活动之前和期间开发的材料将被整理并在线共享，既可以作为个人培训的独立材料，也可以作为未来暑期学校的基础。

The exascale computing landscape in the UK is at an exciting stage,with funding being allocated to novel architectures, new softwareframeworks and innovative algorithms. Through training RSEs we have anopportunity to embed the progress made in these areas into the core ofacademic research and industrial applications, positioning the UK asan international leader in exascale simulations. To grasp thisopportunity is essential that RSEs are trained in algorithms sothat they can take an active part in research in this area. In orderto make informed and creative design choices when writing andoptimising software, RSEs need to have core knowledge of algorithms sothat they can confidently innovate and avoid the common pitfalls thatacademics and industrial partners are already aware of through theirresearch and experience. If this core knowledge is not passed on toRSEs and shared throughout the RSE community, advances made throughthe ExCALIBUR programme research projects risk failing to achievecrucial impact in academic and industrial applications.As described in section 7.3 of the RSE Knowledge Integration LandscapeReview, it is crucial that RSEs have the potential to be activelyinvolved in research. This is one of the key attractions of the jobfor skilled postgraduate students and is essential for retainingskilled RSEs in the role. The Landscape Review acknowledges thatdesign of new algorithms is a research field in itself and requires`strong domain specific knowledge'. We propose to provide training inthis area, alongside opportunities for knowledge exchange andnetworking between academic researchers, postgraduate students, RSEsand industrial partners.We propose to run two three-day workshops and a Summer School toprovide training in state of the art algorithms and core knowledge ofthe underlying foundational mathematical and numerical analysis onwhich they are based. The materials developed in advance of, andduring, these events will be curated and shared online to either beused as stand alone material for individual training or to form thebasis of future summer schools.