Crop Diversity GPU - Growing Plant Understanding

作物多样性GPU -种植植物了解弹性粮食系统和全球植物生物多样性保护（CD-GPU）在英国各地的研究所精心建立和策划了广泛遗传多样性的植物生物材料的全面收集，例如由我们的项目合作伙伴在自然历史博物馆和位于邱园和爱丁堡的皇家植物园策划的国家收藏，以及NIAB用于作物预育种的各种材料。詹姆斯·赫顿研究所（JHI）和苏格兰农村学院（SRUC）。这些资源正越来越多地通过应用高通量“组学”方法来开发，旨在捕获与它们所显示的特征相关的大型复杂数据集，如DNA序列（“基因组学”）、植物特征（从整个田地的水平，一直到单个植物组织和细胞；“表型组学”），以及基因和蛋白质表达的数量和性质（“转录组学”和“蛋白质组学”）。这些方法支撑着广泛的植物研发领域，从景观尺度的分析一直到单个基因及其变异的基础研究。此外，在工业作物生产系统中正在进行一场技术革命，利用增加的作物监测与机器人技术相结合来简化作物生产，为基于真实生产场景的详细时空数据集的学术-工业联合研究提供了进一步的机会。这需要强大的硬件，以便充分利用这些学科内部和之间的研究接口产生的数据集和信息。具体来说，计算基础设施必须包含图形处理单元（gpu）、中央处理单元（CPU）和存储，以提供这种规模的数据集分析所需的适当任务并行化和内存带宽，并通过新兴的机器学习（ML）和人工智能（AI）方法支持它们的分析和解释。CD-GPU将为在植物和作物科学互补领域工作的七个英国研究所组成的联盟提供计算能力。具体来说，它将提供以下硬件：1)人工智能和机器学习方法的GPU容量增加300% 2)“组学”数据和相关建模的存储容量增加65% 3)CPU容量增加55%，以满足高内存生物信息学应用的需求，如全基因组规模的植物基因组组装资源将为我们承担的研究工作提供一个步骤变化，以帮助支持可持续的粮食生产。了解和扭转世界各地植物生物多样性的丧失。重要的是，通过为我们共同的研究需求量身定制CD-GPU，并通过向资源用户提供相关的技术支持和培训，该项目将建立一个具有互补研究目标的强大用户社区，并有助于鼓励我们七个研究所之间的合作和创新。最后，尽管运营这种资源会产生环境成本，但它所带来的植物、作物和农业科技有助于推动实现净零排放。位于单个站点的共享基础设施，而不是在每个研究所分离资源，使计算配置合理化，从而减少安装、维护和资源对环境的影响。我们选择的硬件优先考虑的是单位能耗的性能，而不是具有较少非常快内核的处理器，我们选择具有许多内核且运行效率更高的处理器-非常适合我们的许多目标分析任务，其中并行作业可以实现巨大的成本和性能优势。同样，一个拥有数千个并行核的GPU——在适当的时候——可以比数百个cpu更快、更有效地执行相同的工作。

Crop Diversity GPU - Growing Plant Understanding for resilient food systems and global plant biodiversity conservation (CD-GPU)Comprehensive collections of plant biological materials that capture wide genetic diversity have been carefully established and curated at institutes across the UK, such as the national collections curated by our project partners at the Natural History Museum and the Royal Botanic Gardens in Kew and Edinburgh, as well as diverse materials for crop pre-breeding at NIAB, the James Hutton Institute (JHI) and Scotland's Rural College (SRUC).Such resources are increasingly being exploited via the application of high-throughput 'omics' approaches aimed at capturing large and complex datasets relating to the features they display such as DNA sequence ('genomics'), plant characteristics (from the level of whole fields, down to individual plant tissues and cells; 'phenomics'), and the amount and nature of gene and protein expression ('transcriptomics' and 'proteinomics'). Such approaches underpin broad areas of plant R&D, from landscape-scale analysis all the way down to fundamental research of individual genes and their variants. Further, there is a technological revolution underway within industrial crop production systems, whereby increased crop monitoring combined with robotic technologies are being exploited to streamline crop production - providing further opportunity for academic-industrial joint research underpinned by detailed temporal and spatial datasets from real production scenarios. This requires powerful hardware in order to fully exploit the resulting datasets and information, both within and at the research interface between these disciplines. Specifically, computing infrastructures must incorporate graphical processing units (GPUs), alongside central processing units (CPU) and storage, to provide the appropriate task parallelisation and memory bandwidth required for the analysis of datasets at this scale, as well as to support their analysis and interpretation via emerging Machine Learning (ML) and Artificial Intelligence (AI) approaches.CD-GPU will provide compute capabilities to a consortium of seven UK institutes working in complementary areas of plant and crop science. Specifically, it will deliver the following hardware:1) A 300% increase in GPU capacity for artificial intelligence and machine learning methods2) A 65% increase in storage capacity for 'omics' data and associated modelling3) A 55% increase in CPU capacity to meet the demand for high memory bioinformatics applications such as plant genome assemblies at pangenome scaleThe resource will provide a step-change in the research work we undertake to help underpin sustainable food production, and to understand and reverse plant biodiversity loss across the world. Importantly, by tailoring CD-GPU to our common research needs, and via the provision of associated technical support and training to the users of the resource, this project will build a strong user community with complementary research aims and help encourage collaboration and innovation between our seven institutes.Finally, while there is an environmental cost to run such a resource, the plant, crop and agri-tech science it will enable helps the drive to net zero. Shared infrastructure located at a single site, rather that separate resources at each institute, rationalises compute provision, so reducing environmental impact of installing, maintaining and the resource. The hardware we select is prioritised on performance-per-unit-energy-used, and rather than processors with fewer very fast cores, we select ones with a lot of cores that run more efficiently - a perfect fit for many of our targeted analysis tasks where parallelizing jobs can realise huge cost and performance benefits. Similarly, a single GPU with its thousands of parallel cores can - when appropriate - perform the same work both faster and more efficiently than hundreds of CPUs.