CRII: OAC: A Framework for Parallel Data-Intensive Computing on Emerging Architectures and Astroinformatics Applications

由于当前和未来的仪器和传感器收集的数据量越来越大，需要科学界分析的数据量正在增加。因此，科学数据分析需要大量的时间。为了减少分析数据所需的时间，方法需要利用更多的计算资源，例如计算机中的更多处理器。虽然现代计算机中的中央处理单元(CPU)传统上用于执行数据分析，但在过去十年中，使用图形处理单元(GPU)进行数据分析的情况有所增加。现代图形处理单元包含数千个处理器，它们可以用来比在CPU上更快地执行程序。然而，许多用于数据分析的算法并没有在更广泛的计算机系统的背景下充分利用GPU的潜力。该项目提出了一个框架，用于了解应用于数据分析应用程序的GPU的性能。该项目的主要目标是弥合只使用GPU的算法和充分利用CPU和GPU优势的完全集成算法之间的差距。该项目中探索的一整套算法支持天文学社区和需要高效数据分析方法的其他科学领域的研究人员的需求。该项目旨在实现影响计算机科学和其他科学领域的CPU/GPU计算的新纪元。该项目的一个成果是为教育工作者开发了数据分析和并行计算的交叉点教学材料。该项目包括对K-12、本科生和研究生的指导。因此，正如NSF的使命所述，该项目通过促进科学进步，促进国家健康和繁荣，服务于国家利益。需要新的网络基础设施，如用于新兴异质体系结构的数据分析算法，以解决尖端科学问题。数据分析构建块和算法存在许多依赖于数据的性能瓶颈。新的体系结构有可能缓解其中的一些关键瓶颈。然而，大多数GPU研究最少涉及CPU/主机，并且在GPU上执行大部分计算。这是一个错失的机会，无法更紧密地集成CPU和GPU之间的数据和任务并行性，从而同时利用两种架构之间的并发性。这个项目研究了数据库、机器学习、数据挖掘和并行计算社区中的一系列关键算法。使用这些算法，该项目探索了纯GPU和混合混合并行(CPU和GPU之间的数据和任务并行)之间的连续体，以确定可以通过利用未充分利用的资源来减少的关键瓶颈。选定的算法是科学数据处理工作流程的基础，可以推进时间域天文学网络基础设施。该项目将数据密集型计算的见解整合到本科生和研究生的课程中，并开发了教学模块，供教师用于传授跨CPU和GPU的混合(数据和任务)并行的概念。该项目包括指导本科生、研究生和K-12阶段的学生，包括在科学节上进行推广，以鼓励学生对科学、技术、工程和数学的参与和兴趣。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The amount of data that needs to be analyzed by the scientific community is increasing due to growing volumes of data collected by current and future instruments and sensors. Consequently, scientific data analysis requires a significant amount of time. To decrease the amount of time needed to analyze data, methods need to utilize more computational resources, such as more processors in a computer. While the central processing unit (CPU) in a modern computer has traditionally been used to carry out data analysis, the past decade has seen an increase in using graphics processing units (GPUs) for data analysis. The modern graphics processing unit contains thousands of processors that can be used to execute a program faster than on the CPU. However, many algorithms for data analysis do not use the GPU to its full potential within the context of the broader computer system. This project advances a framework for understanding the performance of GPUs as applied to data analysis applications. The major goal of the project is to bridge the gap between algorithms that only use the GPU, and fully integrated algorithms that exploit the strengths of both the CPU and GPU. The ensemble of algorithms that are explored in the project support the needs of the astronomy community and researchers in other scientific areas that require efficient data analysis methods. The project aims to realize a new era in CPU/GPU computing that impacts both computer science and other scientific fields. An outcome of the project is the development of materials for educators teaching at the intersection of data analysis and parallel computing. This project includes mentoring K-12, undergraduate, and graduate students. Consequently, the project serves the national interest, as stated by NSF's mission, by promoting the progress of science, and to advance the national health and prosperity. New cyberinfrastructure, such as data analysis algorithms for emerging heterogeneous architectures, are needed to address cutting-edge scientific problems. Data analysis building blocks and algorithms have many data-dependent performance bottlenecks. New architectures have the potential to alleviate some of these key bottlenecks. However, the majority of GPU research minimally involves the CPU/host, and performs most of the computation on the GPU. This is a missed opportunity to more closely integrate both data and task parallelism between the CPU and GPU to simultaneously exploit concurrency across both architectures. This project examines a selection of key algorithms in the database, machine learning, data mining, and parallel computing communities. Using these algorithms, this project explores the continuum between GPU-only and mixed hybrid parallelism (data and task parallelism between the CPU and GPU) to identify key bottlenecks that can be reduced by exploiting underutilized resources. The selected algorithms are fundamental to scientific data processing workflows, and can advance time-domain astronomy cyberinfrastructure. The project integrates data-intensive computing insights into courses at the undergraduate and graduate levels, and pedagogical modules are developed to be used by instructors for teaching concepts of mixed (data and task) parallelism across the CPU and GPU. This project includes mentoring students at the undergraduate, graduate, and K-12 levels, including outreach at science festivals to encourage participation and interest in science, technology, engineering, and mathematics.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.