Pub Date : 2012-11-10DOI: 10.1109/SC.Companion.2012.141
P. Lankford, L. Ericson, Andrey Nikolaev
Market risk management is a critical, resourceintensive task for financial trading firms. The industry relies heavily on innovation in technical infrastructure to increase the quality and quantity of risk management information and to reduce the cost of its production. However, until recently, the industry has lacked an independent standard for gauging the potential of new technologies to help. This changed when the STAC BenchmarkTM Council developed STAC-A2TM, a vendorindependent benchmark suite based on real-world market risk analysis workloads. It was specified by trading firms and made actionable by leading HPC vendors. Unlike vendor-developed benchmarks known to the authors, STAC-A2 satisfies all of the requirements important to end-user firms: relevance, neutrality, scalability, and completeness. Intel has demonstrated the utility of STAC-A2 for comparing successive generations of Intel® Xeon® processors.
{"title":"End-User Driven Technology Benchmarks Based on Market-Risk Workloads","authors":"P. Lankford, L. Ericson, Andrey Nikolaev","doi":"10.1109/SC.Companion.2012.141","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.141","url":null,"abstract":"Market risk management is a critical, resourceintensive task for financial trading firms. The industry relies heavily on innovation in technical infrastructure to increase the quality and quantity of risk management information and to reduce the cost of its production. However, until recently, the industry has lacked an independent standard for gauging the potential of new technologies to help. This changed when the STAC BenchmarkTM Council developed STAC-A2TM, a vendorindependent benchmark suite based on real-world market risk analysis workloads. It was specified by trading firms and made actionable by leading HPC vendors. Unlike vendor-developed benchmarks known to the authors, STAC-A2 satisfies all of the requirements important to end-user firms: relevance, neutrality, scalability, and completeness. Intel has demonstrated the utility of STAC-A2 for comparing successive generations of Intel® Xeon® processors.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"47 1","pages":"1171-1175"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91206941","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.2012.149
Joseph Jupin, Justin Y. Shi, Z. Obradovic
For health and human services, fraud detection and other security services, identity resolution is a core requirement for understanding big data in the cloud. Due to the lack of a globally unique identifier and captured typographic differences for the same identity, identity resolution has high spatial and temporal complexities. We propose a filter and verify method to substantially increase the speed of approximate string matching using edit distance. This method has been found to be almost 80 times faster (130 times when combined with other optimizations) than Damerau-Levenshtein edit distance and preserves all approximate matches. Our method creates compressed signatures for data fields and uses Boolean operations and an enhanced bit counter to quickly compare the distance between the fields. This method is intended to be applied to data records whose fields contain relatively short-length strings, such as those found in most demographic data. Without loss of accuracy, the proposed Fast Bitwise Filter will provide substantial performance gain to approximate string comparison in database, record linkage and deduplication data processing systems.
{"title":"Understanding Cloud Data Using Approximate String Matching and Edit Distance","authors":"Joseph Jupin, Justin Y. Shi, Z. Obradovic","doi":"10.1109/SC.Companion.2012.149","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.149","url":null,"abstract":"For health and human services, fraud detection and other security services, identity resolution is a core requirement for understanding big data in the cloud. Due to the lack of a globally unique identifier and captured typographic differences for the same identity, identity resolution has high spatial and temporal complexities. We propose a filter and verify method to substantially increase the speed of approximate string matching using edit distance. This method has been found to be almost 80 times faster (130 times when combined with other optimizations) than Damerau-Levenshtein edit distance and preserves all approximate matches. Our method creates compressed signatures for data fields and uses Boolean operations and an enhanced bit counter to quickly compare the distance between the fields. This method is intended to be applied to data records whose fields contain relatively short-length strings, such as those found in most demographic data. Without loss of accuracy, the proposed Fast Bitwise Filter will provide substantial performance gain to approximate string comparison in database, record linkage and deduplication data processing systems.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"59 1","pages":"1234-1243"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73140971","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.2012.122
M. Ellsworth
This presentation provides a tutorial on ASHRAE thermal guidelines for both air and liquid cooling.
本报告提供了ASHRAE空气和液体冷却热指南的教程。
{"title":"New ASHRAE Thermal Guidelines for Air and Liquid Cooling","authors":"M. Ellsworth","doi":"10.1109/SC.Companion.2012.122","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.122","url":null,"abstract":"This presentation provides a tutorial on ASHRAE thermal guidelines for both air and liquid cooling.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"39 1","pages":"942-961"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73600053","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.2012.365
P. Baumann
Summary form only given. The paper presents the Array Databases using the example of rasdaman, a fully implemented system in operational service since years. We introduce an array query language which embeds seamlessly into standard SQL and show how this language can be supported by a streamlined architecture which allows for effective storage and query optimization and parallelization. In this context we emphasize that Array Database research can gain a lot from combining the knowledge of database, supercomputing, and programming language domains.
{"title":"Array Databases","authors":"P. Baumann","doi":"10.1109/SC.Companion.2012.365","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.365","url":null,"abstract":"Summary form only given. The paper presents the Array Databases using the example of rasdaman, a fully implemented system in operational service since years. We introduce an array query language which embeds seamlessly into standard SQL and show how this language can be supported by a streamlined architecture which allows for effective storage and query optimization and parallelization. In this context we emphasize that Array Database research can gain a lot from combining the knowledge of database, supercomputing, and programming language domains.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"28 1","pages":"1329-1329"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74073467","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.2012.283
R. Jacob, Jayesh Krishna, Xiabing Xu, S. Mickelson, T. Tautges, M. Wilde, R. Latham, Ian T Foster, R. Ross, M. Hereld, J. Larson, P. Bochev, K. Peterson, M. Taylor, K. Schuchardt, Jain Yin, D. Middleton, Mary Haley, David Brown, Wei Huang, D. Shea, R. Brownrigg, M. Vertenstein, K. Ma, Jingrong Xie
Climate models are both outputting larger and larger amounts of data and are doing it on more sophisticated numerical grids. The tools climate scientists have used to analyze climate output, an essential component of climate modeling, are single threaded and assume rectangular structured grids in their analysis algorithms. We are bringing both task- and data-parallelism to the analysis of climate model output. We have created a new data-parallel library, the Parallel Gridded Analysis Library (ParGAL) which can read in data using parallel I/O, store the data on a compete representation of the structured or unstructured mesh and perform sophisticated analysis on the data in parallel. ParGAL has been used to create a parallel version of a script-based analysis and visualization package. Finally, we have also taken current workflows and employed task-based parallelism to decrease the total execution time.
{"title":"Poster: Bringing Task and Data Parallelism to Analysis of Climate Model Output","authors":"R. Jacob, Jayesh Krishna, Xiabing Xu, S. Mickelson, T. Tautges, M. Wilde, R. Latham, Ian T Foster, R. Ross, M. Hereld, J. Larson, P. Bochev, K. Peterson, M. Taylor, K. Schuchardt, Jain Yin, D. Middleton, Mary Haley, David Brown, Wei Huang, D. Shea, R. Brownrigg, M. Vertenstein, K. Ma, Jingrong Xie","doi":"10.1109/SC.Companion.2012.283","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.283","url":null,"abstract":"Climate models are both outputting larger and larger amounts of data and are doing it on more sophisticated numerical grids. The tools climate scientists have used to analyze climate output, an essential component of climate modeling, are single threaded and assume rectangular structured grids in their analysis algorithms. We are bringing both task- and data-parallelism to the analysis of climate model output. We have created a new data-parallel library, the Parallel Gridded Analysis Library (ParGAL) which can read in data using parallel I/O, store the data on a compete representation of the structured or unstructured mesh and perform sophisticated analysis on the data in parallel. ParGAL has been used to create a parallel version of a script-based analysis and visualization package. Finally, we have also taken current workflows and employed task-based parallelism to decrease the total execution time.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"12 1","pages":"1495"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76674227","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.2012.17
David Goodell, S. Kim, R. Latham, M. Kandemir, R. Ross
High-performance computing (HPC) storage systems typically consist of an object storage system that is accessed via the POSIX file interface. However, rapid increases in system scales and storage system complexity have uncovered a number of limitations in this model. In particular, applications and libraries are limited in their ability to partition data into units with independent concurrency control, and mapping complex science data models into the POSIX file model is inconvenient at best. In this paper we propose an alternative interface for use by applications and libraries that provides direct access to underlying storage objects. This model allows applications and libraries to organize storage access around these objects in order to avoid lock contention without needing to create many separate files. Additionally, complex data models are more readily organized into multiple object data streams, simplifying the storage of variable-length data and allowing a choice of degree of parallelism related to access needs. Our approach provides for datasets stored in this new model to coexist with POSIX files, allowing evolution to the new model over time. We apply these concepts in the PVFS, PLFS, and Parallel netCDF packages to prototype the model and describe our experiences.
{"title":"An Evolutionary Path to Object Storage Access","authors":"David Goodell, S. Kim, R. Latham, M. Kandemir, R. Ross","doi":"10.1109/SC.Companion.2012.17","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.17","url":null,"abstract":"High-performance computing (HPC) storage systems typically consist of an object storage system that is accessed via the POSIX file interface. However, rapid increases in system scales and storage system complexity have uncovered a number of limitations in this model. In particular, applications and libraries are limited in their ability to partition data into units with independent concurrency control, and mapping complex science data models into the POSIX file model is inconvenient at best. In this paper we propose an alternative interface for use by applications and libraries that provides direct access to underlying storage objects. This model allows applications and libraries to organize storage access around these objects in order to avoid lock contention without needing to create many separate files. Additionally, complex data models are more readily organized into multiple object data streams, simplifying the storage of variable-length data and allowing a choice of degree of parallelism related to access needs. Our approach provides for datasets stored in this new model to coexist with POSIX files, allowing evolution to the new model over time. We apply these concepts in the PVFS, PLFS, and Parallel netCDF packages to prototype the model and describe our experiences.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"108 1","pages":"36-41"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74661813","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.2012.338
Jordi Ros-Giralt, Péter Szilágyi, R. Lethin
This paper addresses the problem of scalable cyber-security using a cloud computing architecture. Scalability is treated in two contexts: (1) performance and power efficiency and (2) degree of cyber security-relevant information detected by the cyber-security cloud (CSC). We provide a framework to construct CSCs, which derives from a set of fundamental building blocks (forwarders, analyzers and grounds) and the identification of the smallest functional units (atomic CSC cells or simply aCS C cells) capable of embedding the full functionality of the cyber-security cloud. aCSC cells are then studied and several high-performance algorithms are presented to optimize the system's performance and power efficiency. Among these, a new queuing policy - called tail early detection (TED) - is introduced to proactively drop packets in a way that the degree of detected information is maximized while saving power by avoiding spending cycles on less relevant traffic components. We also show that it is possible to use aCSC cells as core building blocks to construct arbitrarily large cyber-security clouds by structuring the cells using a hierarchical architecture. To demonstrate the utility of our framework, we implement one cyber-security "mini-cloud" on a single chip prototype based on the Tilera's TILEPro64 processor demonstrating performance of up to 10Gbps.
{"title":"Scalable Cyber-Security for Terabit Cloud Computing","authors":"Jordi Ros-Giralt, Péter Szilágyi, R. Lethin","doi":"10.1109/SC.Companion.2012.338","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.338","url":null,"abstract":"This paper addresses the problem of scalable cyber-security using a cloud computing architecture. Scalability is treated in two contexts: (1) performance and power efficiency and (2) degree of cyber security-relevant information detected by the cyber-security cloud (CSC). We provide a framework to construct CSCs, which derives from a set of fundamental building blocks (forwarders, analyzers and grounds) and the identification of the smallest functional units (atomic CSC cells or simply aCS C cells) capable of embedding the full functionality of the cyber-security cloud. aCSC cells are then studied and several high-performance algorithms are presented to optimize the system's performance and power efficiency. Among these, a new queuing policy - called tail early detection (TED) - is introduced to proactively drop packets in a way that the degree of detected information is maximized while saving power by avoiding spending cycles on less relevant traffic components. We also show that it is possible to use aCSC cells as core building blocks to construct arbitrarily large cyber-security clouds by structuring the cells using a hierarchical architecture. To demonstrate the utility of our framework, we implement one cyber-security \"mini-cloud\" on a single chip prototype based on the Tilera's TILEPro64 processor demonstrating performance of up to 10Gbps.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"1 1","pages":"1607-1616"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76311938","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}
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.2012.51
S. Khaitan, J. McCalley
Simulation plays a very crucial role to model, study and experiment with any design innovation proposed in the power systems. Since mathematical modeling of power systems leads to tens of thousands of stiff DAEs (differential and algebraic equations), the design of power system simulators involve exercising a trade-off between the simulation speed and modeling accuracy. Lack of efficient and detailed simulators forces the designers to experiment their techniques with small test systems and hence, the results obtained from such experiments may not be representative of the results obtained using real-life power systems. In this paper, we present TDPSS, a high speed time domain power system simulator for dynamic security assessment. TDPSS has been designed using object-oriented programming framework, and thus, it is modular and extensible. By offering a variety of models of power system components and fast numerical algorithms, it provides the user with the flexibility to experiment with different design options in an efficient manner. We discuss the design of TDPSS to give insights into the simulation infrastructure and also discuss the areas where TDPSS can be extended for parallel contingency analysis. We also validate it against the commercial power system simulators, namely PSSE and DSA Tools. Further, we compare the simulation speed of TPDSS for different numerical algorithms. The results have shown that TDPSS is accurate and also outperforms the commonly used commercial simulator PSSE in terms of its computational efficiency.
{"title":"TDPSS: A Scalable Time Domain Power System Simulator for Dynamic Security Assessment","authors":"S. Khaitan, J. McCalley","doi":"10.1109/SC.Companion.2012.51","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.51","url":null,"abstract":"Simulation plays a very crucial role to model, study and experiment with any design innovation proposed in the power systems. Since mathematical modeling of power systems leads to tens of thousands of stiff DAEs (differential and algebraic equations), the design of power system simulators involve exercising a trade-off between the simulation speed and modeling accuracy. Lack of efficient and detailed simulators forces the designers to experiment their techniques with small test systems and hence, the results obtained from such experiments may not be representative of the results obtained using real-life power systems. In this paper, we present TDPSS, a high speed time domain power system simulator for dynamic security assessment. TDPSS has been designed using object-oriented programming framework, and thus, it is modular and extensible. By offering a variety of models of power system components and fast numerical algorithms, it provides the user with the flexibility to experiment with different design options in an efficient manner. We discuss the design of TDPSS to give insights into the simulation infrastructure and also discuss the areas where TDPSS can be extended for parallel contingency analysis. We also validate it against the commercial power system simulators, namely PSSE and DSA Tools. Further, we compare the simulation speed of TPDSS for different numerical algorithms. The results have shown that TDPSS is accurate and also outperforms the commonly used commercial simulator PSSE in terms of its computational efficiency.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"14 1","pages":"323-332"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80103585","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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