XPS: CLCCA: Scalable Parallelism for Irregular and Graph Applications

XPS：CLCCA：不规则和图形应用程序的可扩展并行性

• 批准号: 1335466
• 负责人: Mark Oskin
• 金额: $ 74.96万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1335466&HistoricalAwards=false
• 关键词: XPS; CLCCA; Scalable; Parallelism; Irregular

Irregular applications are increasing in importance to the computing industry. For a long time they have underpinned computations of interest to the national security arm of the federal government. But now, they underpin such questions as ad placement in social networks, and analysis of complex data-sets in medicine and science. For example, developing custom drug therapies involves analysis of how cellular pathways, known drug/patient outcomes, and a particular patient's DNA interact. These complex big-data problems require new computational models in order to execute effectively on commodity hardware. The defining characteristic of these applications is poor locality and massive available parallelism. The key idea is use the available concurrency to tolerate memory latency, instead of relying on locality. Using a highly optimized runtime system, tuned for use on commodity processors and networking hardware it has been shown that scalable performance on these applications can exceed custom supercomputer-class hardware.The research currently being undertaken is to continue exploring and developing this latency tolerant scale-out runtime system. The proposed research directions include exploring ways to mitigate latency for disk (SSD) access, in order to tackle petabyte-scale problems on small clusters of commodity hardware. In addition the research will examine programmable router hardware in order to scale network aggregation into the thousand-node-plus cluster range. Finally the research effort will focus on enhanced language semantics and compiler techniques that make it easier to implement the types of analyses and graph algorithms of interest to the scientific, business, medical, and government communities.

不规则应用程序对计算行业的重要性日益增加。 很长一段时间以来，他们一直支持联邦政府国家安全部门感兴趣的计算。但现在，它们成为社交网络中广告投放、医学和科学中复杂数据集分析等问题的基础。例如，开发定制药物疗法涉及分析细胞途径、已知药物/患者结果以及特定患者的DNA如何相互作用。 这些复杂的大数据问题需要新的计算模型才能在商用硬件上有效执行。 这些应用程序的定义特征是较差的局部性和大量可用的并行性。 其核心思想是使用可用的并发来容忍内存延迟，而不是依赖于局部性。 使用高度优化的运行时系统，调优用于商品处理器和网络硬件，它已被证明，这些应用程序的可扩展性能可以超过定制的超级计算机级硬件。目前正在进行的研究是继续探索和开发这种延迟容忍横向扩展运行时系统。 拟议的研究方向包括探索减少磁盘（SSD）访问延迟的方法，以解决商品硬件小集群上的PB级问题。 此外，该研究还将研究可编程路由器硬件，以便将网络聚合扩展到千节点以上的集群范围。 最后，研究工作将集中在增强的语言语义和编译器技术，使其更容易实现感兴趣的科学，商业，医疗和政府社区的分析和图形算法的类型。