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

OAC 核心：SHF：SMALL：ICURE——针对超大规模架构的压缩或摘要表示的原位分析

• 批准号: 2034850
• 负责人: Gagan Agrawal
• 金额: $ 50万
• 依托单位: AUGUSTA UNIVERSITY RESEARCH INSTITUTE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034850&HistoricalAwards=false
• 关键词: OAC; Core; SHF; SMALL; ICURE

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.

高性能计算系统（HPC）一直在提供迅速增加的计算能力。但是，这种增长也导致了记忆和数据运动带宽相对较低的系统。 这使得分析科学模拟的数据非常具有挑战性。 出现了一种称为原位分析的范式。 该项目通过使用可以称为同构压缩的东西进一步改善了该范式。 同态压缩的想法是以一种可以在压缩数据上直接执行查询的方式（无需减压）。 该项目正在开发这种压缩方法，开发技术以有效地执行图形处理单元（GPU），使用此类压缩表示形式查询处理的技术，最后是一个整体系统，该系统将简化原地分析实现的开发。 总体而言，该项目将对HPC即将到来的系统更有效地对模拟的数据进行分析。 该项目将旨在通过本科和研究生的研究生直接参与项目开发团队的直接参与来扩大计算的参与。 高性能计算系统（HPC）一直在提供迅速增加的计算能力。但是，这种增长也导致了记忆和数据运动带宽相对较低的系统。 这使得分析科学模拟的数据非常具有挑战性。 出现了一种称为原位分析的范式。 该项目通过使用可以称为同构压缩的东西进一步改善了该范式。 同态压缩的想法是以一种可以在压缩数据上直接执行查询的方式（无需减压）。 由此产生的框架即可促进对加速器本身的原位分析，减少分析的总体内存需求，降低总数据移动成本，甚至降低执行分析的时间成本。 实现ICURE的目标涉及许多公开挑战。首先是摘要结构及其构造的选择。该项目具有两个不同的摘要或简洁表示形式：位图索引和集成值索引。第二个问题是使用摘要和压缩表示的分析方法，其中重点是将这些表示形式用于各种分析任务：计算聚合，相关性，基于价值的连接，时间步长选择，时间段选择和有趣的子区域分析。 第三个问题是自动化位置和质量。在考虑仿真和分析之间提供最低干扰的考虑的驱动下，该项目自动化了有关在HPC系统节点中放置特定分析操作和数据的决定。同样，执行了由所需精度和分析的开销驱动的采样水平的自动选择。该项目将旨在通过来自代表性不足的小组的本科和研究生直接参与项目开发团队的直接参与计算。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。

项目成果

MoHA: A Composable System for Efficient In-Situ Analytics on Heterogeneous HPC Systems

MoHA：用于对异构 HPC 系统进行高效原位分析的可组合系统

• 发表时间: 2020
• 期刊: SC 2020
• 作者: Xing, Haoyuan;Agrawal, Gagan;Ramnath, Rajiv
• 通讯作者: Ramnath, Rajiv

Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations

为所有对最短路径 (APSP) 计算缩放和选择 GPU 方法

• DOI: 10.1109/ipdps53621.2022.00027
• 发表时间: 2022
• 期刊: Proceedings of the International Parallel and Distributed Processing Symposium
• 作者: Xia, Yang;Jiang, Peng;Agrawal, Gagan;Ramnath, Rajiv
• 通讯作者: Ramnath, Rajiv

GCD2: A Globally Optimizing Compiler for Mapping DNNs to Mobile DSPs

• DOI: 10.1109/micro56248.2022.00044
• 发表时间: 2022-10
• 期刊: 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)
• 作者: Wei Niu;Jiexiong Guan;Xipeng Shen;Yanzhi Wang;G. Agrawal;Bin Ren

DNNFusion: Accelerating Deep Neural Networks Execution with Advanced Operator Fusion

• DOI: 10.1145/3453483.3454083
• 发表时间: 2021-01-01
• 期刊: PROCEEDINGS OF THE 42ND ACM SIGPLAN INTERNATIONAL CONFERENCE ON PROGRAMMING LANGUAGE DESIGN AND IMPLEMENTATION (PLDI '21)
• 作者: Niu, Wei;Guan, Jiexiong;Ren, Bin
• 通讯作者: Ren, Bin

GPU Adaptive In-situ Parallel Analytics (GAP)

• DOI: 10.1145/3559009.3569661
• 发表时间: 2022-01-01
• 期刊: PROCEEDINGS OF THE 2022 31ST INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURES AND COMPILATION TECHNIQUES, PACT 2022
• 作者: Xing，Haoyuan;Agrawal，Gagan;Ramnath，Rajiv
• 通讯作者: Ramnath，Rajiv