CAREER: A Highly Effective, Usable, Performant, Scalable Data Reduction Framework for HPC Systems and Applications

职业：适用于 HPC 系统和应用程序的高效、可用、高性能、可扩展的数据缩减框架

• 批准号: 2232120
• 负责人: Dingwen Tao
• 金额: $ 46.78万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232120&HistoricalAwards=false
• 关键词: CAREER; Highly; Effective; Usable; Performant; CAREER; Highly; Effective; Usable; Performant

This CAREER project researches and develops novel algorithms and software to improve the efficacy, usability, performance, and scalability of data reduction for high-performance computing (HPC) systems and applications. It contributes to the cyberinfrastructure (CI) of big data management for HPC applications in many domains such as cosmology, climatology, seismology, and machine learning. The research findings will be widely disseminated through open-source software packages and publications in premier conferences and journals. An integrated educational and outreach program is designed to foster CI workforce development, including integration of concepts and use of data reduction in curricula, research training for undergraduate and graduate students, and a specially designed training program for scientists and engineers from universities and national labs.This CAREER project simultaneously addresses these four critical issues in scientific data reduction through comprehensive analytical modeling and architectural performance optimization. Specific scientific contributions include: (1) it builds lightweight models to accurately estimate the compression ratio and quality of different techniques in the prediction and encoding stages of prediction-based compression, and optimizes the compression configurations to maximize the compression ratio under compression quality constraints; (2) it develops new efficient predictors and lossless encoding methods for lossy compression of scientific data on GPUs with deep architectural optimizations to achieve both high throughput and ratio; and (3) it deeply integrates the optimized compression with parallel I/O and MPI libraries with a series of optimizations to improve the performance of data movements and the scalability of HPC applications. The success of this research agenda enables scientists and engineers to well address the increasingly severe challenge of scientific data explosion.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.

该职业项目研究并开发了新颖的算法和软件，以提高高性能计算（HPC）系统和应用程序数据降低的功效，可用性，性能和可扩展性。它为许多领域中的HPC应用程序（例如宇宙学，气候学，地震学和机器学习）中的HPC应用程序的网络基础设施（CI）做出了贡献。研究发现将通过首屈一指的会议和期刊中的开源软件包和出版物广泛传播。一项综合的教育和外展计划旨在促进CI劳动力发展，包括概念的整合和减少课程的数据，针对本科生和研究生的研究培训，以及针对大学和国家实验室的科学家和工程师的专门设计的培训计划，这同时通过综合分析和建筑型制造能力降低了这些四个批判性数据，以解决这些四个批判性数据降低。具体的科学贡献包括：（1）它构建了轻质模型，以准确估计基于预测的压缩的预测和编码阶段的不同技术的压缩比和质量，并优化压缩构型以在压缩质量质量约束下最大化压缩比； （2）它开发了新的有效预测因子和无损编码方法，以通过深层建筑优化的GPU损失科学数据，以实现高吞吐量和比率； （3）它将优化的压缩与并行I/O和MPI库与一系列优化深入整合，以提高数据运动的性能和HPC应用程序的可扩展性。这项研究议程的成功使科学家和工程师能够很好地解决科学数据爆炸的日益严重挑战。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。