OAC Core: SHF: SMALL: ICURE -- In-situ Analytics with Compressed or Summary Representations for Extreme-Scale Architectures

高性能计算系统（HPC）提供了快速增长的计算能力。然而，这种增长也导致了内存和数据移动带宽相对较低的系统。这使得分析来自科学模拟的数据非常具有挑战性。一种称为原位分析的范式应运而生。这个项目通过使用所谓的同态压缩进一步改进了这个范例。同态压缩的思想是以一种可以直接在压缩数据上执行查询的方式压缩数据（不需要解压缩）。该项目正在开发这种压缩方法，开发在图形处理单元（gpu）上有效执行这种压缩的技术，使用这种压缩表示进行查询处理的技术，最后，一个将简化原位分析实现开发的整体系统。总的来说，这个项目将在即将到来的HPC系统上更有效地分析模拟数据。该项目将寻求通过来自代表性不足群体的本科生和研究生直接参与项目开发团队来扩大对计算机的参与。高性能计算系统（HPC）提供了快速增长的计算能力。然而，这种增长也导致了内存和数据移动带宽相对较低的系统。这使得分析来自科学模拟的数据非常具有挑战性。一种称为原位分析的范式应运而生。这个项目通过使用所谓的同态压缩进一步改进了这个范例。同态压缩的思想是以一种可以直接在压缩数据上执行查询的方式压缩数据（不需要解压缩）。由此产生的框架ICURE可以促进加速器本身的现场分析，减少分析的总体内存需求，降低总数据移动成本，甚至减少执行分析的时间成本。实现ICURE的目标涉及许多公开的挑战。首先是摘要结构和结构的选择。本项目试验了两种不同的摘要或简洁表示：位图索引和集成值索引。第二个问题是使用摘要表示和压缩表示的分析方法，其中重点是将这些表示用于各种分析任务：计算聚合、相关性、基于值的连接、时间步选择和感兴趣的子区域分析。第三个问题是自动化放置和质量。考虑到在模拟和分析之间提供最低的干扰，该项目自动化了在HPC系统节点内放置特定分析操作和数据的决策。同样，由所需的精度和分析开销驱动的采样水平的自动选择被执行。该项目将寻求通过来自代表性不足群体的本科生和研究生直接参与项目开发团队来扩大对计算机的参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Systems for High Performance Computing (HPC) have been providing rapidly increasing computing power. However, this growth has also led to systems where the memory and data movement bandwidth is relatively lower. This makes analyzing the data from scientific simulations very challenging. A paradigm called in-situ analytics has emerged in response. This project is further improving this paradigm, by using what can be referred to as homomorphic compressions. The idea of homomorphic compression is to compress the data in a way that queries can be directly executed on the compressed data (without need for decompression). This project is developing such compression methods, developing techniques to perform such compression efficiently on Graphic Processing Units (GPUs), techniques for query processing using such compressed representations, and finally, an overall system that will simplify development of in-situ analytics implementations. Overall, this project will be making analysis of data from simulations more effective on the upcoming systems for HPC. This project will seek to broaden participation in computing through direct participation in the project development teams by undergraduate and graduate students from under-represented groups. Systems for High Performance Computing (HPC) have been providing rapidly increasing computing power. However, this growth has also led to systems where the memory and data movement bandwidth is relatively lower. This makes analyzing the data from scientific simulations very challenging. A paradigm called in-situ analytics has emerged in response. This project is further improving this paradigm, by using what can be referred to as homomorphic compressions. The idea of homomorphic compression is to compress the data in a way that queries can be directly executed on the compressed data (without need for decompression). The resulting framework, ICURE, can facilitate in situ analytics on accelerators themselves, reduce overall memory requirements for the analytics, reduce total data movements costs, and even reduce the time cost of performing the analytics. Achieving the goals of ICURE involves many open challenges. The first is the choice of summarization structure and its constructions. This project experiments with two different summary or concise representations: bitmap indices and an integrated value index. The second issue is analyses methods using summary and compressed representations, where the focus is on the use of these representations for a variety of analyses tasks: computing aggregations, correlations, value-based joins, time-step selection, and interesting subregions analysis. The third issue is automating placement and quality. Driven by the consideration of providing the lowest interference between the simulation and analytics, this project automates decisions on placement of specific analytics operations and data within the node of HPC system. Similarly, automatic selection of sampling level driven by desired accuracy and overheads of the analyses is performed. This project will seek to broaden participation in computing through direct participation in the project development teams by undergraduate and graduate students from under-represented groups.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.