{"title":"Turbulent Length Scale for Multilayer RANS Model of Urban Canopy and Its Evaluation Based on Large-Eddy Simulations","authors":"A. Glazunov, A. Debolskiy, E. Mortikov","doi":"10.14529/jsfi210409","DOIUrl":"https://doi.org/10.14529/jsfi210409","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134454971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Technology for Supercomputer Simulation of Turbulent Flows in the Good New Days of Exascale Computing","authors":"A. Gorobets, A. Duben","doi":"10.14529/jsfi210401","DOIUrl":"https://doi.org/10.14529/jsfi210401","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133988852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Drobot, Evgeny M Kirilin, K. Kopylov, V. Svedas
{"title":"PLUMED Plugin Integration into High Performance Pmemd Program for Enhanced Molecular Dynamics Simulations","authors":"V. Drobot, Evgeny M Kirilin, K. Kopylov, V. Svedas","doi":"10.14529/jsfi210408","DOIUrl":"https://doi.org/10.14529/jsfi210408","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121474887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving the Computational Efficiency of the Global SL-AV Numerical Weather Prediction Model","authors":"M. Tolstykh, R. Fadeev, V. Shashkin, G. Goyman","doi":"10.14529/jsfi210402","DOIUrl":"https://doi.org/10.14529/jsfi210402","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122591755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Representation of Spatial Data Processing Pipelines Using Relational Database","authors":"I. Okladnikov","doi":"10.14529/jsfi210404","DOIUrl":"https://doi.org/10.14529/jsfi210404","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115163681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Stefanov, Sucheta Pawar, Ashish Ranjan, Sanjay Wandhekar, V. Voevodin
High Performance Computing is now one of the emerging fields in computer science and its applications. Top HPC facilities, supercomputers, offer great opportunities in modeling diverse processes thus allowing to create more and greater products without full-scale experiments. Current supercomputers and applications for them are very complex and thus are hard to use efficiently. Performance monitoring systems are the tools that help to understand the efficiency of supercomputing applications and overall supercomputer functioning. These systems collect data on what happens on a supercomputer (performance data, performance metrics) and present them in a way allowing to make conclusions about performance issues in programs running on the supercomputer. In this paper we give an overview of existing performance monitoring systems designed for or used on supercomputers. We give a comparison of performance monitoring systems found in literature, describe problems emerging in monitoring large scale HPC systems, and outline our vision on future direction of HPC monitoring systems development.
{"title":"A Review of Supercomputer Performance Monitoring Systems","authors":"K. Stefanov, Sucheta Pawar, Ashish Ranjan, Sanjay Wandhekar, V. Voevodin","doi":"10.14529/jsfi210304","DOIUrl":"https://doi.org/10.14529/jsfi210304","url":null,"abstract":"High Performance Computing is now one of the emerging fields in computer science and its applications. Top HPC facilities, supercomputers, offer great opportunities in modeling diverse processes thus allowing to create more and greater products without full-scale experiments. Current supercomputers and applications for them are very complex and thus are hard to use efficiently. Performance monitoring systems are the tools that help to understand the efficiency of supercomputing applications and overall supercomputer functioning. These systems collect data on what happens on a supercomputer (performance data, performance metrics) and present them in a way allowing to make conclusions about performance issues in programs running on the supercomputer. In this paper we give an overview of existing performance monitoring systems designed for or used on supercomputers. We give a comparison of performance monitoring systems found in literature, describe problems emerging in monitoring large scale HPC systems, and outline our vision on future direction of HPC monitoring systems development.","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124194367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Afanasyev, D. I. Lichmanov, V. Rudyak, V. Voevodin
In this paper we demonstrate the process of efficient porting a software package for Markov chain Monte Carlo (MCMC) simulations on a finite cubic lattice on multiple modern architectures: Pascal, Volta and Turing NVIDIA GPUs, NEC SX-Aurora TSUBASA vector engines and Intel Xeon Gold processors. In the studied software, MCMC methodology is used for simulations of liquid crystal structures, but it can be as well employed in a wide range of problems of mathematical physics and numerical methods. The main goals of this work are to determine the best software optimization strategy for this class of algorithms and to examine the speed and the efficiency of such simulations on modern HPC platforms. We evaluate the effects of various optimizations, such as using more suitable memory access patterns, multitasking for efficient utilization of massive parallelism on the target architectures, improved cache hit-rates, parallel workload balancing, etc. We perform a detailed performance analysis for each target platform using software tools such as nvprof, Ftrace and VTune. On this basis, we evaluate and compare the efficiency of the developed computational kernels on different platforms and subsequently rank these platforms by their performance. The results show that NVIDIA GPU and NEC SX-Aurora TSUBASA platforms, although at first glance seem very different, require similar optimization approaches in many cases due to similarities in data processing principles.
{"title":"Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms","authors":"I. Afanasyev, D. I. Lichmanov, V. Rudyak, V. Voevodin","doi":"10.14529/jsfi210306","DOIUrl":"https://doi.org/10.14529/jsfi210306","url":null,"abstract":"In this paper we demonstrate the process of efficient porting a software package for Markov chain Monte Carlo (MCMC) simulations on a finite cubic lattice on multiple modern architectures: Pascal, Volta and Turing NVIDIA GPUs, NEC SX-Aurora TSUBASA vector engines and Intel Xeon Gold processors. In the studied software, MCMC methodology is used for simulations of liquid crystal structures, but it can be as well employed in a wide range of problems of mathematical physics and numerical methods. The main goals of this work are to determine the best software optimization strategy for this class of algorithms and to examine the speed and the efficiency of such simulations on modern HPC platforms. We evaluate the effects of various optimizations, such as using more suitable memory access patterns, multitasking for efficient utilization of massive parallelism on the target architectures, improved cache hit-rates, parallel workload balancing, etc. We perform a detailed performance analysis for each target platform using software tools such as nvprof, Ftrace and VTune. On this basis, we evaluate and compare the efficiency of the developed computational kernels on different platforms and subsequently rank these platforms by their performance. The results show that NVIDIA GPU and NEC SX-Aurora TSUBASA platforms, although at first glance seem very different, require similar optimization approaches in many cases due to similarities in data processing principles.","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130868845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The solution of systems of linear algebraic equations is among the time-consuming problems when performing the numerical simulations. One of the possible ways of improving the corresponding solver performance is the use of reduced precision calculations, which, however, may affect the accuracy of the obtained solution. The current paper analyzes the potential of using the mixed precision iterative refinement procedure to solve the systems of equations occurring as a result of the discretization of elliptic differential equations. The paper compares several inner solver stopping criteria and proposes the one allowing to eliminate the residual deviation and minimize the number of extra iterations. The presented numerical calculation results demonstrate the efficiency of the adopted algorithm and show about the decrease in the solution time by a factor of 1.5 for the turbulent flow simulations when using the iterative refinement procedure to solve the corresponding pressure Poisson equation.
{"title":"Evaluating Performance of Mixed Precision Linear Solvers with Iterative Refinement","authors":"B. Krasnopolsky, A. Medvedev","doi":"10.14529/jsfi210301","DOIUrl":"https://doi.org/10.14529/jsfi210301","url":null,"abstract":"The solution of systems of linear algebraic equations is among the time-consuming problems when performing the numerical simulations. One of the possible ways of improving the corresponding solver performance is the use of reduced precision calculations, which, however, may affect the accuracy of the obtained solution. The current paper analyzes the potential of using the mixed precision iterative refinement procedure to solve the systems of equations occurring as a result of the discretization of elliptic differential equations. The paper compares several inner solver stopping criteria and proposes the one allowing to eliminate the residual deviation and minimize the number of extra iterations. The presented numerical calculation results demonstrate the efficiency of the adopted algorithm and show about the decrease in the solution time by a factor of 1.5 for the turbulent flow simulations when using the iterative refinement procedure to solve the corresponding pressure Poisson equation.","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127947129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Komatsu, Akito Onodera, E. Focht, Soya Fujimoto, Yoko Isobe, S. Momose, Masayuki Sato, Hiroaki Kobayashi
{"title":"Performance and Power Analysis of a Vector Computing System","authors":"K. Komatsu, Akito Onodera, E. Focht, Soya Fujimoto, Yoko Isobe, S. Momose, Masayuki Sato, Hiroaki Kobayashi","doi":"10.14529/jsfi210205","DOIUrl":"https://doi.org/10.14529/jsfi210205","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Porting and Optimizing Molecular Docking onto the SX-Aurora TSUBASA Vector Computer","authors":"Leonardo Solis-Vasquez, E. Focht, A. Koch","doi":"10.14529/jsfi210202","DOIUrl":"https://doi.org/10.14529/jsfi210202","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122945404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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