Qingcai Jiang , Zhenwei Cao , Xinhui Cui , Lingyun Wan , Xinming Qin , Huanqi Cao , Hong An , Junshi Chen , Jie Liu , Wei Hu , Jinlong Yang
{"title":"用两种不同方法扩展 LR-TDDFT 的极限:数值算法和新型 Sunway 异构超级计算机","authors":"Qingcai Jiang , Zhenwei Cao , Xinhui Cui , Lingyun Wan , Xinming Qin , Huanqi Cao , Hong An , Junshi Chen , Jie Liu , Wei Hu , Jinlong Yang","doi":"10.1016/j.parco.2024.103085","DOIUrl":null,"url":null,"abstract":"<div><p>First-principles time-dependent density functional theory (TDDFT) is a powerful tool to accurately describe the excited-state properties of molecules and solids in condensed matter physics, computational chemistry, and materials science. However, a perceived drawback in TDDFT calculations is its ultrahigh computational cost <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>5</mn></mrow></msup><mo>∼</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>6</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> and large memory usage <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> especially for plane-wave basis set, confining its applications to large systems containing thousands of atoms. Here, we present a massively parallel implementation of linear-response TDDFT (LR-TDDFT) and accelerate LR-TDDFT in two different aspects: (1) numerical algorithms on the X86 supercomputer and (2) optimizations on the heterogeneous architecture of the new Sunway supercomputer. Furthermore, we carefully design the parallel data and task distribution schemes to accommodate the physical nature of different computation steps. By utilizing these two different methods, our implementation can gain an overall speedup of 10x and 80x and efficiently scales to large systems up to 4096 and 2744 atoms within dozens of seconds.</p></div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"120 ","pages":"Article 103085"},"PeriodicalIF":2.0000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extending the limit of LR-TDDFT on two different approaches: Numerical algorithms and new Sunway heterogeneous supercomputer\",\"authors\":\"Qingcai Jiang , Zhenwei Cao , Xinhui Cui , Lingyun Wan , Xinming Qin , Huanqi Cao , Hong An , Junshi Chen , Jie Liu , Wei Hu , Jinlong Yang\",\"doi\":\"10.1016/j.parco.2024.103085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>First-principles time-dependent density functional theory (TDDFT) is a powerful tool to accurately describe the excited-state properties of molecules and solids in condensed matter physics, computational chemistry, and materials science. However, a perceived drawback in TDDFT calculations is its ultrahigh computational cost <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>5</mn></mrow></msup><mo>∼</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>6</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> and large memory usage <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> especially for plane-wave basis set, confining its applications to large systems containing thousands of atoms. Here, we present a massively parallel implementation of linear-response TDDFT (LR-TDDFT) and accelerate LR-TDDFT in two different aspects: (1) numerical algorithms on the X86 supercomputer and (2) optimizations on the heterogeneous architecture of the new Sunway supercomputer. Furthermore, we carefully design the parallel data and task distribution schemes to accommodate the physical nature of different computation steps. By utilizing these two different methods, our implementation can gain an overall speedup of 10x and 80x and efficiently scales to large systems up to 4096 and 2744 atoms within dozens of seconds.</p></div>\",\"PeriodicalId\":54642,\"journal\":{\"name\":\"Parallel Computing\",\"volume\":\"120 \",\"pages\":\"Article 103085\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Parallel Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167819124000231\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Parallel Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167819124000231","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Extending the limit of LR-TDDFT on two different approaches: Numerical algorithms and new Sunway heterogeneous supercomputer
First-principles time-dependent density functional theory (TDDFT) is a powerful tool to accurately describe the excited-state properties of molecules and solids in condensed matter physics, computational chemistry, and materials science. However, a perceived drawback in TDDFT calculations is its ultrahigh computational cost and large memory usage especially for plane-wave basis set, confining its applications to large systems containing thousands of atoms. Here, we present a massively parallel implementation of linear-response TDDFT (LR-TDDFT) and accelerate LR-TDDFT in two different aspects: (1) numerical algorithms on the X86 supercomputer and (2) optimizations on the heterogeneous architecture of the new Sunway supercomputer. Furthermore, we carefully design the parallel data and task distribution schemes to accommodate the physical nature of different computation steps. By utilizing these two different methods, our implementation can gain an overall speedup of 10x and 80x and efficiently scales to large systems up to 4096 and 2744 atoms within dozens of seconds.
期刊介绍:
Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems.
Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results.
Particular technical areas of interest include, but are not limited to:
-System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing).
-Enabling software including debuggers, performance tools, and system and numeric libraries.
-General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems
-Software engineering and productivity as it relates to parallel computing
-Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism
-Performance measurement results on state-of-the-art systems
-Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures.
-Parallel I/O systems both hardware and software
-Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications