Pub Date : 2012-09-01DOI: 10.1109/HPEC.2012.6408675
Tao Cui, F. Franchetti
Solving a large number of load flow problems quickly is required for Monte Carlo analysis and various power system problems, including long term steady state simulation, system benchmarking, among others. Due to the computational burden, such applications are considered to be time-consuming, and infeasible for online or realtime application. In this work we developed a high performance framework for high throughput distribution load flow computation, taking advantage of performance-enhancing features of multi-core CPUs and various code optimization techniques. We optimized data structures to better fit the memory hierarchy. We use the SPIRAL code generator to exploit inherent patterns of the load flow model through code specizlization. We use SIMD instructions and multithreading to parallelize our solver. Finally, we designed a Monte Carlo thread scheduling infrastructure to enable real time operation. The optimized solver is able to achieve more than 50% of peak performance on a Intel Core i7 CPU, which translates to solving millions of load flow problems within a second for IEEE 37 test feeder.
{"title":"Optimized parallel distribution load flow solver on commodity multi-core CPU","authors":"Tao Cui, F. Franchetti","doi":"10.1109/HPEC.2012.6408675","DOIUrl":"https://doi.org/10.1109/HPEC.2012.6408675","url":null,"abstract":"Solving a large number of load flow problems quickly is required for Monte Carlo analysis and various power system problems, including long term steady state simulation, system benchmarking, among others. Due to the computational burden, such applications are considered to be time-consuming, and infeasible for online or realtime application. In this work we developed a high performance framework for high throughput distribution load flow computation, taking advantage of performance-enhancing features of multi-core CPUs and various code optimization techniques. We optimized data structures to better fit the memory hierarchy. We use the SPIRAL code generator to exploit inherent patterns of the load flow model through code specizlization. We use SIMD instructions and multithreading to parallelize our solver. Finally, we designed a Monte Carlo thread scheduling infrastructure to enable real time operation. The optimized solver is able to achieve more than 50% of peak performance on a Intel Core i7 CPU, which translates to solving millions of load flow problems within a second for IEEE 37 test feeder.","PeriodicalId":193020,"journal":{"name":"2012 IEEE Conference on High Performance Extreme Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322026","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 : 2012-09-01DOI: 10.1109/HPEC.2012.6408682
S. Seefeld, Faheem Sheikh, B. Moses
A hybrid MATLAB/C++ programming model for high performance embedded computing is presented. It is shown how the use of a common data model and API can help not only to speed up the development process, but also to keep the original MATLAB model in sync with the evolving C++ code, and thus allowing it to remain a gold standard for the project as it evolves.
{"title":"A MATLAB-to-target development workflow using Sourcery VSIPL++","authors":"S. Seefeld, Faheem Sheikh, B. Moses","doi":"10.1109/HPEC.2012.6408682","DOIUrl":"https://doi.org/10.1109/HPEC.2012.6408682","url":null,"abstract":"A hybrid MATLAB/C++ programming model for high performance embedded computing is presented. It is shown how the use of a common data model and API can help not only to speed up the development process, but also to keep the original MATLAB model in sync with the evolving C++ code, and thus allowing it to remain a gold standard for the project as it evolves.","PeriodicalId":193020,"journal":{"name":"2012 IEEE Conference on High Performance Extreme Computing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130883370","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 : 2012-09-01DOI: 10.1109/HPEC.2012.6408659
I. Kiss, Z. Badics, S. Gyimóthy, J. Pávó
The utilization of Graphical Processing Units (GPUs) for the element-by-element (EbE) finite element method (FEM) is demonstrated. EbE FEM is a long known technique, by which a conjugate gradient (CG) type iterative solution scheme can be entirely decomposed into computations on the element level, i.e., without assembling the global system matrix. In our implementation, NVIDIA's parallel computing solution, the Compute Unified Device Architecture (CUDA), is used to perform the required element-wise computations in parallel. Since element matrices need not be stored, the memory requirement can be kept extremely low. It is shown that this low-storage but computation-intensive technique is better suited for GPUs than those requiring the massive manipulation of large data sets. This study of the proposed parallel model illustrates a highly improved locality and minimization of data movement, which could also significantly reduce energy consumption in other heterogeneous HPC architectures.
{"title":"High locality and increased intra-node parallelism for solving finite element models on GPUs by novel element-by-element implementation","authors":"I. Kiss, Z. Badics, S. Gyimóthy, J. Pávó","doi":"10.1109/HPEC.2012.6408659","DOIUrl":"https://doi.org/10.1109/HPEC.2012.6408659","url":null,"abstract":"The utilization of Graphical Processing Units (GPUs) for the element-by-element (EbE) finite element method (FEM) is demonstrated. EbE FEM is a long known technique, by which a conjugate gradient (CG) type iterative solution scheme can be entirely decomposed into computations on the element level, i.e., without assembling the global system matrix. In our implementation, NVIDIA's parallel computing solution, the Compute Unified Device Architecture (CUDA), is used to perform the required element-wise computations in parallel. Since element matrices need not be stored, the memory requirement can be kept extremely low. It is shown that this low-storage but computation-intensive technique is better suited for GPUs than those requiring the massive manipulation of large data sets. This study of the proposed parallel model illustrates a highly improved locality and minimization of data movement, which could also significantly reduce energy consumption in other heterogeneous HPC architectures.","PeriodicalId":193020,"journal":{"name":"2012 IEEE Conference on High Performance Extreme Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116575010","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 : 2012-09-01DOI: 10.1109/HPEC.2012.6408677
E. Rutledge, B. A. Miller, M. Beard
Graph analysis is used in many domains, from the social sciences to physics and engineering. The computational driver for one important class of graph analysis algorithms is the computation of leading eigenvectors of matrix representations of a graph. This paper explores the computational implications of performing an eigen decomposition of a directed graph's symmetrized modularity matrix using commodity cluster hardware and freely available eigensolver software, for graphs with 1 million to 1 billion vertices, and 8 million to 8 billion edges. Working with graphs of these sizes, parallel eigensolvers are of particular interest. Our results suggest that graph analysis approaches based on eigen space analysis of graph residuals are feasible even for graphs of these sizes.
{"title":"Benchmarking parallel eigen decomposition for residuals analysis of very large graphs","authors":"E. Rutledge, B. A. Miller, M. Beard","doi":"10.1109/HPEC.2012.6408677","DOIUrl":"https://doi.org/10.1109/HPEC.2012.6408677","url":null,"abstract":"Graph analysis is used in many domains, from the social sciences to physics and engineering. The computational driver for one important class of graph analysis algorithms is the computation of leading eigenvectors of matrix representations of a graph. This paper explores the computational implications of performing an eigen decomposition of a directed graph's symmetrized modularity matrix using commodity cluster hardware and freely available eigensolver software, for graphs with 1 million to 1 billion vertices, and 8 million to 8 billion edges. Working with graphs of these sizes, parallel eigensolvers are of particular interest. Our results suggest that graph analysis approaches based on eigen space analysis of graph residuals are feasible even for graphs of these sizes.","PeriodicalId":193020,"journal":{"name":"2012 IEEE Conference on High Performance Extreme Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129401153","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 : 2012-09-01DOI: 10.1109/HPEC.2012.6408679
J. Dongarra, P. Luszczek
We present a complete bottom-up implementation of an embedded LINPACK benchmark on iPad 2. We use a novel formulation of a recursive LU factorization that is recursive and parallel at the global scope. We be believe our new algorithm presents an alternative to existing linear algebra parallelization techniques such as master-worker and DAG-based approaches. We show a assembly API that allows us a much higher level of abstraction and provides rapid code development within the confines of mobile device SDK. We use performance modeling to help with the limitation of the device and the limited access to device from the development environment not geared for HPC application tuning.
{"title":"Anatomy of a globally recursive embedded LINPACK benchmark","authors":"J. Dongarra, P. Luszczek","doi":"10.1109/HPEC.2012.6408679","DOIUrl":"https://doi.org/10.1109/HPEC.2012.6408679","url":null,"abstract":"We present a complete bottom-up implementation of an embedded LINPACK benchmark on iPad 2. We use a novel formulation of a recursive LU factorization that is recursive and parallel at the global scope. We be believe our new algorithm presents an alternative to existing linear algebra parallelization techniques such as master-worker and DAG-based approaches. We show a assembly API that allows us a much higher level of abstraction and provides rapid code development within the confines of mobile device SDK. We use performance modeling to help with the limitation of the device and the limited access to device from the development environment not geared for HPC application tuning.","PeriodicalId":193020,"journal":{"name":"2012 IEEE Conference on High Performance Extreme Computing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966459","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 : 2012-09-01DOI: 10.1109/HPEC.2012.6408670
M. Hubbell, J. Kepner
Obtaining situational awareness of network activity across an enterprise presents unique visualization challenges. IT analysts are required to quickly gather and correlate large volumes of disparate data to identify the existence of anomalous behavior. This paper will show how the MIT Lincoln Laboratory LLGrid Team has approached obtaining network situational awareness utilizing the Unity 3D video game engine. We have developed a 3D environment of the physical plant in the format of a networked multi player First Person Shooter (FPS) to demonstrate a virtual depiction of the current state of the network and the machines operating on the network. Within the game or virtual world an analyst or player can gather critical information on all network assets as well as perform physical system actions on machines in question. 3D gaming technology provides tools to create an environment that is both visually familiar to the player as well display immense amounts of system data in a meaningful and easy to absorb format. Our prototype system was able to monitor and display 5000 assets in ~10% of the time of our network time window.
{"title":"Large scale network situational awareness via 3D gaming technology","authors":"M. Hubbell, J. Kepner","doi":"10.1109/HPEC.2012.6408670","DOIUrl":"https://doi.org/10.1109/HPEC.2012.6408670","url":null,"abstract":"Obtaining situational awareness of network activity across an enterprise presents unique visualization challenges. IT analysts are required to quickly gather and correlate large volumes of disparate data to identify the existence of anomalous behavior. This paper will show how the MIT Lincoln Laboratory LLGrid Team has approached obtaining network situational awareness utilizing the Unity 3D video game engine. We have developed a 3D environment of the physical plant in the format of a networked multi player First Person Shooter (FPS) to demonstrate a virtual depiction of the current state of the network and the machines operating on the network. Within the game or virtual world an analyst or player can gather critical information on all network assets as well as perform physical system actions on machines in question. 3D gaming technology provides tools to create an environment that is both visually familiar to the player as well display immense amounts of system data in a meaningful and easy to absorb format. Our prototype system was able to monitor and display 5000 assets in ~10% of the time of our network time window.","PeriodicalId":193020,"journal":{"name":"2012 IEEE Conference on High Performance Extreme Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129386189","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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