Accelerating Graph Analytics on Clouds for Genome Assembly

图分析形成了一个典型的大数据问题，对从社会科学到基因组学的科学长尾具有重要价值。虽然用于大内存机器的图形算法比比皆是，但它们无法被更广泛的社区访问。在分布式网络基础设施（如云和商品集群）上为图形应用程序开发适当的抽象一直具有挑战性。这项工作探讨了一个以子图为中心的方法，提供了一个数量级的性能优势的潜力。这项工作研究图形算法，专注于从头植物基因组测序，使用可扩展的子图为中心的图编程模型的云。它提供了一个新的研究方向，可以深刻地影响下一代基因组测序，除了其他领域的图形抽象可以采用。它通过大量以子图为中心的算法来催化分布式图分析的研究，减轻延迟采用技术和特定领域计算抽象的机会成本。在这个过程中，它将从根本上推进可扩展的图形处理，以快速加速和民主化下一代测序大数据的网络基础设施。

Graph analytics form a canonical Big Data problem that is of significant value to the long tail of science, from social sciences to genomics. While graph algorithms for big-memory machines abound, they are inaccessible to the wider community. Developing appropriate abstractions for graph applications on distributed cyber-infrastructure like Clouds and commodity clusters has been challenging. This work explores a subgraph-centric approach which offers the potential for an order-of-magnitude performance benefit. This work investigates graph algorithms, focused on de novo plant genome sequencing, that use a scalable subgraph-centric graph programming model for Clouds. It offers a novel research direction that can profoundly impact next-generation genome sequencing in addition to other domains where graph abstractions can be employed. It catalyzes research into distributed graph analytics through a critical mass of subgraph-centric algorithms, mitigating the lost opportunity cost in delayed adoption of the technology and domain specific computing abstractions. In the process, it will fundamentally advance scalable graph processing to rapidly accelerate and democratize cyber-infrastructure for Big Data for next generation sequencing.