CRII: SHF: ACI: Performance-in-Depth Sparse Solvers for Heterogeneous Parallel Platforms.

CRII：SHF：ACI：异构并行平台的深度性能稀疏求解器。

• 批准号: 1657175
• 负责人: Narayan Mandayam
• 金额: $ 17.5万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657175&HistoricalAwards=false
• 关键词: CRII; SHF; ACI; Performance; Depth

Sparse numerical computations are at the heart of many science and engineering simulations. However, the complex irregularities in sparse methods limit the performance of many scientific software. This project integrates mathematical reformulation, algorithm redesign, and performance engineering to develop high-performance sparse solvers for heterogeneous parallel platforms. The outcomes of this research are innovative tools and methodologies that advance the field of large-scale scientific simulations. In addition, the project has a broader impact in training graduate students to perform interdisciplinary research. The project conducts an in-depth investigation of performance bottlenecks in sparse solvers and reformulates their standard variants to deliver end-to-end performance. Cross-layer solutions are developed to improve data locality, reduce communication, and increase inherent parallelism in sparse linear solvers. The solutions involve multi-level algorithm restructuring and performance tuning to significantly improve the scalability and performance of sparse computations while preserving their numerical accuracy, convergence, and stability. The proposed methods and algorithms are implemented as domain-specific high-performance software and a benchmark suite to promote iterative improvements of the developed algorithms and codes.

稀疏的数值计算是许多科学和工程模拟的核心。但是，稀疏方法中的复杂不规则性限制了许多科学软件的性能。该项目集成了数学重新印象，算法重新设计和性能工程，以开发用于异构平行平台的高性能稀疏求解器。这项研究的结果是创新的工具和方法，可以推进大型科学模拟的领域。此外，该项目对培训研究生具有更大的影响，以进行跨学科研究。该项目对稀疏求解器的性能瓶颈进行了深入的调查，并重新制定其标准变体以提供端到端的性能。开发了跨层溶液来改善数据局部性，减少通信并增加稀疏线性求解器中的固有性。该解决方案涉及多级算法重组和性能调整，以显着提高稀疏计算的可扩展性和性能，同时保持其数值准确性，收敛性和稳定性。所提出的方法和算法被实现为特定领域的高性能软件和基准套件，以促进已开发算法和代码的迭代改进。

项目成果

Sympiler: transforming sparse matrix codes by decoupling symbolic analysis

Sympiler：通过解耦符号分析来变换稀疏矩阵码

• DOI: 10.1145/3126908.3126936
• 发表时间: 2017
• 期刊: Storage and Analysis
• 作者: Cheshmi, Kazem;Kamil, Shoaib;Strout, Michelle Mills;Dehnavi, Maryam Mehri
• 通讯作者: Dehnavi, Maryam Mehri

MatRox: modular approach for improving data locality in hierarchical (Mat)rix App(Rox)imation

MatRox：用于改进分层 (Mat)rix App(Rox)imation 中数据局部性的模块化方法

• DOI: 10.1145/3332466.3374548
• 发表时间: 2020
• 期刊: PPoPP '20: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• 作者: Liu, Bangtian;Cheshmi, Kazem;Soori, Saeed;Strout, Michelle Mills;Dehnavi, Maryam Mehri
• 通讯作者: Dehnavi, Maryam Mehri

A Unified Optimization Approach for Sparse Tensor Operations on GPUs

• DOI: 10.1109/cluster.2017.75
• 发表时间: 2017-05
• 期刊: 2017 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: Bangtian Liu;Chengyao Wen;A. Sarwate;M. Dehnavi

ParSy: Inspection and Transformation of Sparse Matrix Computations for Parallelism

• DOI: 10.1109/sc.2018.00065
• 发表时间: 2018-11
• 期刊: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Kazem Cheshmi;Shoaib Kamil;M. Strout;M. Dehnavi

Reducing Communication in Proximal Newton Methods for Sparse Least Squares Problems

• DOI: 10.1145/3225058.3225131
• 发表时间: 2018-08
• 期刊: Proceedings of the 47th International Conference on Parallel Processing
• 作者: Saeed Soori;Aditya Devarakonda;Zachary Blanco;J. Demmel;M. Gürbüzbalaban;M. Dehnavi