CAREER: Communication-efficient and topology-aware designs for geo-spatial analytics on heterogeneous platforms

职业：异构平台上地理空间分析的通信效率和拓扑感知设计

基本信息

批准号：
2145403
负责人：
Satish Puri
金额：
$ 51.12万
依托单位：
Marquette University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2023-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145403&HistoricalAwards=false
关键词：
CAREER Communication efficient topology aware

项目摘要

Geospatial datasets are growing in volume, complexity, and heterogeneity. The current era of geospatial big data combined with advancements in data science is enabling public and private organizations to gain new insights and build new capabilities. Various agencies and scientific communities rely on spatial data management and analysis to gain insights and produce actionable plans. This project will design efficient algorithms for spatial data analytics that can serve as a crucial tool for solving a wide set of research problems from different scientific areas. The algorithms and implementations will leverage high performance computing (HPC) to speedup compute and data intensive workloads in domains that employ spatial data. The computational geometry algorithms chosen are broadly applicable in various domains that make use of spatial data, including sociology, epidemiology, pathology, climate science, solar physics, etc. Undergraduate and graduate students will be trained to carry out high performance computing research. Educational materials relevant to the research agenda will be developed and disseminated through educational workshops in the parallel computing and spatial computing domains.Most of the existing work in the literature for geospatial analytics is limited to exploiting basic thread-level and data parallelism. Existing work on I/O efficient algorithms are mostly based on shared memory parallelism and data on disk. Data movement due to communication among processors is a dominant cost incurred by an application running on a high performance computing (HPC) system. We propose communication efficient designs for geospatial analytics on heterogeneous platforms. A second area of research is topology aware designs for spatial computing systems that can seamlessly leverage Data Processing Units (DPUs). A DPU is a new class of coprocessor that evolved as a successor to programmable smart network interface cards. This project will develop distributed memory parallel algorithms for filter and refine-based spatial analytics kernels. Motivated by the heterogeneity in a compute node, there is a new opportunity for design space exploration for geospatial applications. New parallelization techniques based on communication vs. computation tradeoffs will be investigated. The new design will leverage network topology aware spatial data partitioning and remote direct memory access (RDMA) capability in DPUs for implementing hierarchical filter and refine techniques. The implementation of parallel algorithms will be incorporated into publicly available MPI GIS software. These libraries will be implemented to be scalable in heterogeneous computing systems. For broader impact, the new software libraries will be integrated with CyberGIS geospatial packages.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

地理空间数据集的数量，复杂性和异质性正在增长。当前的地理空间大数据时代结合了数据科学的进步，使公共和私人组织能够获得新的见解并建立新的能力。各种机构和科学社区都依靠空间数据管理和分析来获得见解并制定可行的计划。该项目将为空间数据分析设计有效的算法，这些算法可以作为解决不同科学领域的广泛研究问题的关键工具。这些算法和实现将利用高性能计算（HPC）来加速计算使用空间数据的域中的数据密集型工作负载。所选的计算几何算法广泛适用于使用空间数据的各个领域，包括社会学，流行病学，病理学，气候科学，太阳能物理学等。本科生和研究生将接受培训，以进行高性能计算研究。与研究议程相关的教育材料将通过平行计算和空间计算领域的教育研讨会进行开发和传播。地理空间分析文献中现有的大多数工作都仅限于利用基本线程级别和数据并行性。 I/O有效算法的现有工作主要基于共享内存并行性和磁盘上的数据。由于处理器之间的通信而导致的数据移动是在高性能计算（HPC）系统上运行的应用程序所产生的主要成本。我们建议在异质平台上为地理空间分析的沟通高效设计。第二个研究领域是可以无缝利用数据处理单元（DPU）的空间计算系统的拓扑意识设计。 DPU是一类新的协处理器，后来成为可编程智能网络接口卡的继任者。该项目将开发用于过滤器和基于完善的空间分析核的分布式内存并行算法。由计算节点中的异质性激励，有一个新的机会用于地理空间应用的设计空间探索。将研究基于通信与计算折衷的新的并行化技术。新设计将利用网络拓扑了解DPU中的空间数据分配和远程直接内存访问（RDMA）功能，以实现层次结构过滤器和完善技术。并行算法的实现将纳入公开可用的MPI GIS软件中。这些库将在异质计算系统中实现。为了更大的影响，新的软件库将与网络地理空间套件集成。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。