Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.138
M. Creel, M. Zubair
In this paper, we describe a GPU based implementation for an estimator based on an indirect likelihood inference method. This method relies on simulations from a model and on nonparametric density or regression function computations. The estimation application arises in various domains such as econometrics and finance, when the model is fully specified, but too complex for estimation by maximum likelihood. We implemented the estimator on a machine with two 2.67GHz Intel Xeon X5650 processors and four NVIDIA M2090 GPU devices. We optimized the GPU code by efficient use of shared memory and registers available on the GPU devices. We compared the optimized GPU code performance with a C based sequential version of the code that was executed on the host machine. We observed a speed up factor of up to 242 with four GPU devices.
{"title":"High Performance Implementation of an Econometrics and Financial Application on GPUs","authors":"M. Creel, M. Zubair","doi":"10.1109/SC.Companion.2012.138","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.138","url":null,"abstract":"In this paper, we describe a GPU based implementation for an estimator based on an indirect likelihood inference method. This method relies on simulations from a model and on nonparametric density or regression function computations. The estimation application arises in various domains such as econometrics and finance, when the model is fully specified, but too complex for estimation by maximum likelihood. We implemented the estimator on a machine with two 2.67GHz Intel Xeon X5650 processors and four NVIDIA M2090 GPU devices. We optimized the GPU code by efficient use of shared memory and registers available on the GPU devices. We compared the optimized GPU code performance with a C based sequential version of the code that was executed on the host machine. We observed a speed up factor of up to 242 with four GPU devices.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"os-27 1","pages":"1147-1153"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87212408","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.145
J. M. Reddy, J. Monika
Cloud computing is known as a novel information technology (IT) concept, which involves facilitated and rapid access to networks, servers, data saving media, applications and services via Internet with minimum hardware requirements. Use of information systems and technologies at the battlefield is not new. Information superiority is a force multiplier and is crucial to mission success. Distributed cloud computing in the Military systems are operational today. In the near future extensive use of military clouds at the battlefield is predicted. Integrating cloud computing logic to military applications will increase the flexibility, cost-effectiveness, efficiency and accessibility capabilities. In this paper, distributed cloud computing concepts are defined. Cloud computing supported battlefield applications are analyzed. The effects of cloud computing systems on the information domain in future warfare are discussed. Battlefield opportunities and novelties which might be introduced by distributed cloud computing systems are researched. The role of military clouds in future warfare is proposed in this paper. It was concluded that military clouds will be indispensible components of the future battlefield. Military clouds have the potential of increasing situational awareness at the battlefield and facilitating the settlement of information superiority.
{"title":"Integrate Military with Distributed Cloud Computing and Secure Virtualization","authors":"J. M. Reddy, J. Monika","doi":"10.1109/SC.Companion.2012.145","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.145","url":null,"abstract":"Cloud computing is known as a novel information technology (IT) concept, which involves facilitated and rapid access to networks, servers, data saving media, applications and services via Internet with minimum hardware requirements. Use of information systems and technologies at the battlefield is not new. Information superiority is a force multiplier and is crucial to mission success. Distributed cloud computing in the Military systems are operational today. In the near future extensive use of military clouds at the battlefield is predicted. Integrating cloud computing logic to military applications will increase the flexibility, cost-effectiveness, efficiency and accessibility capabilities. In this paper, distributed cloud computing concepts are defined. Cloud computing supported battlefield applications are analyzed. The effects of cloud computing systems on the information domain in future warfare are discussed. Battlefield opportunities and novelties which might be introduced by distributed cloud computing systems are researched. The role of military clouds in future warfare is proposed in this paper. It was concluded that military clouds will be indispensible components of the future battlefield. Military clouds have the potential of increasing situational awareness at the battlefield and facilitating the settlement of information superiority.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"86 1","pages":"1200-1206"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82205997","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.57
Hormozd Gahvari, W. Gropp, K. E. Jordan, M. Schulz, U. Yang
The IBM Blue Gene/Q represents a large step in the evolution of massively parallel machines. It features 16-core compute nodes, with additional parallelism in the form of four simultaneous hardware threads per core, connected together by a five-dimensional torus network. Machines are being built with core counts in the hundreds of thousands, with the largest, Sequoia, featuring over 1.5 million cores. In this paper, we develop a performance model for the solve cycle of algebraic multigrid on Blue Gene/Q to help us understand the issues this popular linear solver for large, sparse linear systems faces on this architecture. We validate the model on a Blue Gene/Q at IBM, and conclude with a discussion of the implications of our results.
{"title":"Performance Modeling of Algebraic Multigrid on Blue Gene/Q: Lessons Learned","authors":"Hormozd Gahvari, W. Gropp, K. E. Jordan, M. Schulz, U. Yang","doi":"10.1109/SC.Companion.2012.57","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.57","url":null,"abstract":"The IBM Blue Gene/Q represents a large step in the evolution of massively parallel machines. It features 16-core compute nodes, with additional parallelism in the form of four simultaneous hardware threads per core, connected together by a five-dimensional torus network. Machines are being built with core counts in the hundreds of thousands, with the largest, Sequoia, featuring over 1.5 million cores. In this paper, we develop a performance model for the solve cycle of algebraic multigrid on Blue Gene/Q to help us understand the issues this popular linear solver for large, sparse linear systems faces on this architecture. We validate the model on a Blue Gene/Q at IBM, and conclude with a discussion of the implications of our results.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"39 3 1","pages":"377-385"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79906465","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.83
K. W. Smith, W. Spotz, S. Ross-Ross
We present three Python software projects: PyTrilinos, for calling Trilinos distributed memory HPC solvers from Python; Optimized Distributed NumPy (ODIN), for distributed array computing; and Seamless, for automatic, Just-in-time compilation of Python source code. We argue that these three projects in combination provide a framework for high-performance computing in Python. They provide this framework by supplying necessary features (in the case of ODIN and Seamless) and algorithms (in the case of ODIN and PyTrilinos) for a user to develop HPC applications. Together they address the principal limitations (real or imagined) ascribed to Python when applied to high-performance computing. A high-level overview of each project is given, including brief explanations as to how these projects work in conjunction to the benefit of end users.
{"title":"A Python HPC Framework: PyTrilinos, ODIN, and Seamless","authors":"K. W. Smith, W. Spotz, S. Ross-Ross","doi":"10.1109/SC.Companion.2012.83","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.83","url":null,"abstract":"We present three Python software projects: PyTrilinos, for calling Trilinos distributed memory HPC solvers from Python; Optimized Distributed NumPy (ODIN), for distributed array computing; and Seamless, for automatic, Just-in-time compilation of Python source code. We argue that these three projects in combination provide a framework for high-performance computing in Python. They provide this framework by supplying necessary features (in the case of ODIN and Seamless) and algorithms (in the case of ODIN and PyTrilinos) for a user to develop HPC applications. Together they address the principal limitations (real or imagined) ascribed to Python when applied to high-performance computing. A high-level overview of each project is given, including brief explanations as to how these projects work in conjunction to the benefit of end users.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"34 1","pages":"593-599"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89436812","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.287
Hang Liu, J. Seo, R. Mittal
Finding a fast solver for the Poisson equation is important for many scientific applications. In this work, we design and develop a matrix decomposition based Conjugate Gradient (CG) solver, which leverages Graphics Processing Unit (GPU) clusters to accelerate the calculation of the Poisson equation. Our experiments show that the new CG solver is highly scalable and achieves significant speedup over a CPU-based Multi-Grid (MG) solver.
{"title":"Poster: Matrix Decomposition Based Conjugate Gradient Solver for Poisson Equation","authors":"Hang Liu, J. Seo, R. Mittal","doi":"10.1109/SC.Companion.2012.287","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.287","url":null,"abstract":"Finding a fast solver for the Poisson equation is important for many scientific applications. In this work, we design and develop a matrix decomposition based Conjugate Gradient (CG) solver, which leverages Graphics Processing Unit (GPU) clusters to accelerate the calculation of the Poisson equation. Our experiments show that the new CG solver is highly scalable and achieves significant speedup over a CPU-based Multi-Grid (MG) solver.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"18 1","pages":"1501-1501"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89500732","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.29
A. Chervenak, David E. Smith, Weiwei Chen, E. Deelman
As scientific applications generate and consume data at ever-increasing rates, scientific workflow systems that manage the growing complexity of analyses and data movement will increase in importance. The goal of our work is to improve the overall performance of scientific workflows by using policy to improve data staging into and out of computational resources. We developed a Policy Service that gives advice to the workflow system about how to stage data, including advice on the order of data transfers and on transfer parameters. The Policy Service gives this advice based on its knowledge of ongoing transfers, recent transfer performance, and the current allocation of resources for data staging. The paper describes the architecture of the Policy Service and its integration with the Pegasus Workflow Management System. It employs a range of policies for data staging, and presents performance results for one policy that does a greedy allocation of data transfer streams between source and destination sites. The results show performance improvements for a data-intensive workflow: the Montage astronomy workflow augmented to perform additional large data staging operations.
{"title":"Integrating Policy with Scientific Workflow Management for Data-Intensive Applications","authors":"A. Chervenak, David E. Smith, Weiwei Chen, E. Deelman","doi":"10.1109/SC.Companion.2012.29","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.29","url":null,"abstract":"As scientific applications generate and consume data at ever-increasing rates, scientific workflow systems that manage the growing complexity of analyses and data movement will increase in importance. The goal of our work is to improve the overall performance of scientific workflows by using policy to improve data staging into and out of computational resources. We developed a Policy Service that gives advice to the workflow system about how to stage data, including advice on the order of data transfers and on transfer parameters. The Policy Service gives this advice based on its knowledge of ongoing transfers, recent transfer performance, and the current allocation of resources for data staging. The paper describes the architecture of the Policy Service and its integration with the Pegasus Workflow Management System. It employs a range of policies for data staging, and presents performance results for one policy that does a greedy allocation of data transfer streams between source and destination sites. The results show performance improvements for a data-intensive workflow: the Montage astronomy workflow augmented to perform additional large data staging operations.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"28 1","pages":"140-149"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90303692","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.124
Nicolas Dubé
This presentation debunks three "truths" as seen from Plato's cave: the untold story of PUE, clean coal, and water is free and available.
这个演讲揭穿了从柏拉图的洞穴中看到的三个“真理”:PUE的不为人知的故事，清洁煤，水是免费的。
{"title":"Philosophy 301: But Can You \"Handle the Truth\"?","authors":"Nicolas Dubé","doi":"10.1109/SC.Companion.2012.124","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.124","url":null,"abstract":"This presentation debunks three \"truths\" as seen from Plato's cave: the untold story of PUE, clean coal, and water is free and available.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"19 1","pages":"993-1017"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90699826","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.358
B. Walkup
This article consists of a collection of slides from the author's conference presentation. The author concludes that The Blue Gene/Q design, low-power simple cores, four hardware threads per core, resu lts in high instruction throughput, and thus exceptional power efficiency for applications. Can effectively fill in pipeline stalls and hide latencies in the memory subsystem. The consequence is low performance per thread, so a high degree of parallelization is required for high application performance. Traditional programming methods (MPI, OpenMP, Pthreads) hold up at very large scales. Memory costs can limit scaling when there are data-structures with size linear in the number of processes, threading helps by keeping the number of processes manageable. Detailed performance analysis is viable at > 10^6 processes but requires care. On-the-fly performance data reduction has merits.
{"title":"Application performance characterization and analysis on Blue Gene/Q","authors":"B. Walkup","doi":"10.1109/SC.Companion.2012.358","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.358","url":null,"abstract":"This article consists of a collection of slides from the author's conference presentation. The author concludes that The Blue Gene/Q design, low-power simple cores, four hardware threads per core, resu lts in high instruction throughput, and thus exceptional power efficiency for applications. Can effectively fill in pipeline stalls and hide latencies in the memory subsystem. The consequence is low performance per thread, so a high degree of parallelization is required for high application performance. Traditional programming methods (MPI, OpenMP, Pthreads) hold up at very large scales. Memory costs can limit scaling when there are data-structures with size linear in the number of processes, threading helps by keeping the number of processes manageable. Detailed performance analysis is viable at > 10^6 processes but requires care. On-the-fly performance data reduction has merits.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"77 1","pages":"2247-2280"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80791774","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.364
Bradley Carvey, Nathan Fabian, D. Rogers
The animation shows a simulation of an explosive charge, blowing a hold in a steel plate. The simulation data was generated on Sandia National Lab's Red Sky Supercomputer. ParaView was used to export polygonal data, which was then textured and rendered using a commercial 3d rendering package.
{"title":"Explosive Charge Blowing a Hole in a Steel Plate Animation","authors":"Bradley Carvey, Nathan Fabian, D. Rogers","doi":"10.1109/SC.Companion.2012.364","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.364","url":null,"abstract":"The animation shows a simulation of an explosive charge, blowing a hold in a steel plate. The simulation data was generated on Sandia National Lab's Red Sky Supercomputer. ParaView was used to export polygonal data, which was then textured and rendered using a commercial 3d rendering package.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"101 1","pages":"1576-1577"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80416795","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}
Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.65
T. Janjusic, K. Kavi, Christos Kartsaklis
As the complexity of scientific codes and computational hardware increases it is increasingly important to study the effects of data-structure layouts on program memory behavior. Program structure layouts affect the memory performance differently, therefore we need the capability to effectively study such transformations without the need to rewrite application codes. Trace-driven simulations are an effective and convenient mechanism to simulate program behavior at various granularities. During an application's execution, a tool known as a tracer or profiler, collects program flow data and records program instructions. The trace-file consists of tuples that associate each program instruction with program internal variables. In this paper we outline a proof-of-concept mechanism to apply data-structure transformations during trace simulation and observe effects on memory without the need to manually transform an application's code.
{"title":"Trace Driven Data Structure Transformations","authors":"T. Janjusic, K. Kavi, Christos Kartsaklis","doi":"10.1109/SC.Companion.2012.65","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.65","url":null,"abstract":"As the complexity of scientific codes and computational hardware increases it is increasingly important to study the effects of data-structure layouts on program memory behavior. Program structure layouts affect the memory performance differently, therefore we need the capability to effectively study such transformations without the need to rewrite application codes. Trace-driven simulations are an effective and convenient mechanism to simulate program behavior at various granularities. During an application's execution, a tool known as a tracer or profiler, collects program flow data and records program instructions. The trace-file consists of tuples that associate each program instruction with program internal variables. In this paper we outline a proof-of-concept mechanism to apply data-structure transformations during trace simulation and observe effects on memory without the need to manually transform an application's code.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"146 1","pages":"456-464"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76443786","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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