CAREER: Exploiting Parallel Heterogeneous Architectures to Enable Time-domain Astronomy in the LSST era

最近和不久的将来，科学仪器将产生大量数据。这种仪器的一个例子是维拉·C·鲁宾天文台，它将在十年内进行传统的空间和时间调查(LSST)。这项天文调查有可能推动许多天文学领域的发展，甚至可能导致新的科学研究领域的发展。然而，庞大的数据量意味着需要在合理的时间内使用许多处理器来处理数据。这个项目创造了可以利用大量处理器的新技术和算法。特别是，该项目利用了标准中央处理器(CPU)和图形处理器(GPU)的能力，这两种处理器擅长同时处理许多数据项。开发的技术旨在利用LSST的数据，发现太阳系中有趣的事件。一旦在给定的天文天体上探测到有趣的事件，就会向天文学团体发送警报，以便他们可以使用额外的望远镜来进一步研究这些天体。如果没有这个项目开发的技术，天文学家将错失研究瞬变现象的机会。该项目整合了几项教学活动，以确保计算机科学家和天文学家都得到开发下一代计算机系统所需的培训。该项目包括指导本科生和研究生。此外，当地社区将通过促进科学、技术、工程和数学领域的外联活动参与进来，特别是通过针对K-12学生的活动。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、繁荣和福利，服务于国家利益。维拉·C·鲁宾天文台将拥有前所未有的时间域能力。然而，LSST将产生大量数据，需要对这些数据进行检查，以实现许多科学目标。该项目侧重于LSST在太阳系科学背景下支持网络基础设施(CI)。需要快速的离群值检测，以使其他设施能够快速跟进，以确保发现太阳系中的瞬变事件和具有本质不寻常性质的天体。为了确保快速检测能力，孤立点检测算法将利用不同的CPU和GPU架构。此外，在CPU和GPU之间分配工作的情况下，将采用异构式计算。此外，该项目还研究了在现代GPU硬件上使用特定于应用的集成电路，例如应用于比矩阵乘法和光线跟踪更广泛的应用的张量和光线跟踪核心。利用这些异类处理器需要算法转换；因此，开发了一个统一的框架，该框架对在这些架构上执行的这些算法的性能进行建模。这个框架和新颖的并行和可扩展算法提供了基础性的CI，使LSST能够成功地探索太阳系，了解其起源，并识别潜在的危险小行星，以及其他科学目标。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。

Recent and near future scientific instruments will generate large amounts of data. One example of such an instrument is the Vera C. Rubin Observatory that will carry out the Legacy Survey of Space and Time (LSST) over a ten year period. This astronomical survey has the potential to advance many fields of astronomy, and may even lead to the development of new fields of scientific inquiry. However, the large data volume implies that many processors will need to be used to process the data within a reasonable amount of time. This project creates new technologies and algorithms that can utilize a large number of processors. In particular, the project harnesses the power of both standard central processing units (CPUs) and graphics processing units (GPUs) that are good at processing many data items simultaneously. The developed technologies are designed to use the data from LSST and find interesting events in the Solar System. Once an interesting event is detected on a given astronomical object, alerts are sent to the astronomy community so that they can use additional telescopes to further study these objects. Without the technologies developed in this project, astronomers will miss out on opportunities to study transient phenomena. The project integrates several teaching activities that ensure both computer scientists and astronomers receive the necessary training to exploit future generation computer systems. The project includes mentoring undergraduate and graduate students. In addition, the local community will be engaged through outreach activities that promote science, technology, engineering, and mathematical fields, particularly through activities targeting K-12 students. The project serves the national interest, as stated by NSF's mission, by promoting the progress of science, and to advance the national health, prosperity, and welfare. The Vera C. Rubin Observatory will have unprecedented time domain capabilities. However, LSST will generate large volumes of data that need to be examined in order to realize many scientific goals. This project focuses on LSST supporting cyberinfrastructure (CI) in the context of Solar System science. Fast outlier detection is needed to enable rapid follow up by other facilities to ensure that transient events in the Solar System and objects with intrinsically unusual properties are discovered. To ensure rapid detection capabilities, the outlier detection algorithms will exploit heterogeneous CPU and GPU architectures. Furthermore, heterogeneous computing will be employed where the work is distributed between the CPU and GPU. Also, the project examines using application specific integrated circuits on modern GPU hardware, such as tensor and ray tracing cores as applied to a broader range of applications than matrix multiplication and ray tracing. Algorithmic transformations are needed to exploit these heterogeneous processors; consequently, a unifying framework is developed that models the performance of these algorithms as executed on these architectures. This framework and novel parallel and scalable algorithms provide foundational CI that will enable the LSST to successfully explore the Solar System, understand its origins, and identify potentially hazardous asteroids, among other scientific objectives.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.