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.

稀疏数值计算是许多科学和工程模拟的核心。然而，稀疏方法中复杂的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表述、算法重新设计和性能工程，以开发适用于异构型并行平台的高性能稀疏解算器。这项研究的结果是创新的工具和方法，推动了大规模科学模拟领域的发展。此外，该项目在培养研究生进行跨学科研究方面具有更广泛的影响。该项目对稀疏解算器中的性能瓶颈进行了深入的调查，并重新制定了它们的标准变体，以提供端到端的性能。开发跨层解决方案是为了改善数据局部性，减少通信，并增加稀疏线性求解器中的内在并行性。这些解决方案包括多级算法重构和性能调整，以显著提高稀疏计算的可扩展性和性能，同时保持其数值精度、收敛和稳定性。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测试套件，以促进所开发的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