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

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.

该职业项目研究和开发新颖的算法和软件，以提高高性能计算 (HPC) 系统和应用程序的数据缩减的功效、可用性、性能和可扩展性。它为宇宙学、气候学、地震学和机器学习等许多领域的 HPC 应用程序的大数据管理网络基础设施 (CI) 做出了贡献。研究结果将通过开源软件包以及在重要会议和期刊上发表的出版物来广泛传播。综合教育和推广计划旨在促进 CI 劳动力发展，包括在课程中整合概念和使用数据缩减、本科生和研究生的研究培训，以及为大学和国家实验室的科学家和工程师专门设计的培训计划。该职业项目通过全面的分析建模和架构性能优化，同时解决了科学数据缩减中的这四个关键问题。具体的科学贡献包括：（1）构建轻量级模型，准确估计基于预测的压缩的预测和编码阶段不同技术的压缩比和质量，并优化压缩配置，以在压缩质量约束下最大化压缩比； （2）开发新的高效预测器和无损编码方法，用于在 GPU 上对科学数据进行有损压缩，并进行深度架构优化，以实现高吞吐量和高比率； (3)将优化压缩与并行I/O和MPI库深度集成，通过一系列优化来提高数据移动的性能和HPC应用的可扩展性。该研究议程的成功使科学家和工程师能够很好地应对科学数据爆炸日益严峻的挑战。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

• DOI: 10.1145/3588195.3592994
• 发表时间: 2023-04
• 期刊: Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing
• 作者: Bo Zhang;Jiannan Tian;S. Di;Xiaodong Yu;Yunhe Feng;Xin Liang;Dingwen Tao;F. Cappello

TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition

• DOI: 10.1145/3572848.3577478
• 发表时间: 2022-11
• 期刊: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
• 作者: Lizhi Xiang;Miao Yin;Chengming Zhang;Aravind Sukumaran-Rajam;P. Sadayappan;Bo Yuan;Dingwen Tao

AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications

AMRIC：一种新颖的原位有损压缩框架，可在自适应网格细化应用中实现高效 I/O

• DOI: 10.1145/3581784.3613212
• 发表时间: 2023
• 期刊: ACM
• 作者: Wang, Daoce;Pulido, Jesus;Grosset, Pascal;Tian, Jiannan;Jin, Sian;Tang, Houjun;Sexton, Jean;Di, Sheng;Zhao, Kai;Fang, Bo
• 通讯作者: Fang, Bo

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

GPULZ：在现代 GPU 上优化多字节数据的 LZSS 无损压缩

• DOI: 10.1145/3577193.3593706
• 发表时间: 2023
• 期刊: ICS '23: Proceedings of the 37th International Conference on Supercomputing
• 作者: Zhang, Boyuan;Tian, Jiannan;Di, Sheng;Yu, Xiaodong;Swany, Martin;Tao, Dingwen;Cappello, Franck
• 通讯作者: Cappello, Franck

HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs

• DOI: 10.1145/3577193.3593717
• 发表时间: 2023-04
• 期刊: Proceedings of the 37th International Conference on Supercomputing
• 作者: Chengming Zhang;Shaden Smith;Baixi Sun;Jiannan Tian;Jon Soifer;Xiaodong Yu;S. Song;Yuxiong He-Yuxiong