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\n  \n 2015\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Scalability and Power Efficiency of In-situ Analytics Workflows.\n \n \n \n \n\n\n \n Landge, A. G.; Bremer, P.; and Pascucci, V.\n\n\n \n\n\n\n April 2015.\n (INVITED POSTER)\n\n\n\n
\n\n\n\n \n \n \"ScalabilityPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{landge_poster3,\nauthor = {Aaditya G. Landge and Peer-Timo Bremer and Valerio Pascucci},\njournal= {The Salishan Conference on High Speed Computing},\ntitle = {Scalability and Power Efficiency of In-situ Analytics Workflows},\nyear={2015},\nmonth={April},\nvolume={},\nnumber={},\npages={},\nnote = {(INVITED POSTER)},\nurl={posters/salishan2015.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Large Scale In-Situ Analysis of Scientific Simulations.\n \n \n \n \n\n\n \n Landge, A. G.; Pascucci, V.; and Bremer, P.\n\n\n \n\n\n\n August 2015.\n (OUTSTANDING POSTER AWARD)\n\n\n\n
\n\n\n\n \n \n \"LargePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{landge_poster4,\nauthor = {Aaditya G. Landge and Valerio Pascucci and Peer-Timo Bremer },\njournal= {Lawrence Livermore National Laboratory Student Poster Symposium},\ntitle = {Large Scale In-Situ Analysis of Scientific Simulations},\nyear={2015},\nmonth={August},\nvolume={},\nnumber={},\npages={},\nnote = {(OUTSTANDING POSTER AWARD)},\nurl={posters/llnl2015.pdf}\n}\n
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees.\n \n \n \n \n\n\n \n Landge, A. G.; Pascucci, V.; Gyulassy, A.; Bennett, J. C.; Kolla, H.; Chen, J.; and Bremer, P.\n\n\n \n\n\n\n In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, of SC '14, pages 1020–1031, Piscataway, NJ, USA, 2014. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n \n \"In-SituPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{landge_sc14,\nauthor = { Landge, Aaditya G. and Pascucci, Valerio and Gyulassy, Attila and Bennett, Janine C. and Kolla, Hemanth and Chen, Jacqueline and  Bremer, Peer-Timo},\ntitle = {In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees},\nbooktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},\nseries = {SC '14},\nyear = {2014},\nabstract = {The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights.  This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability.  The second is a scalable, local approximation that nevertheless is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime.  },\nisbn = {978-1-4799-5500-8},\n location = {New Orleans, Louisana},\n  pages = {1020--1031},\n   numpages = {12},\n     doi = {10.1109/SC.2014.88},\n      acmid = {2683704},\n       publisher = {IEEE Press},\n        address = {Piscataway, NJ, USA},\n         keywords = {feature extraction, in situ analysis, merge tree\n         computation, segmented merge tree, topological data analysis},\nurl = {papers/pap654s4.pdf},\npublisher = {IEEE Computer Society},\n}\n\n\n
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\n The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights. This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that nevertheless is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime. \n
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\n  \n 2013\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Exploring Power Behaviors and Trade-offs of In-situ Data Analytics.\n \n \n \n \n\n\n \n Gamell, M.; Rodero, I.; Parashar, M.; Bennett, J. C.; Kolla, H.; Chen, J.; Bremer, P.; Landge, A. G.; Gyulassy, A.; McCormick, P.; Pakin, S.; Pascucci, V.; and Klasky, S.\n\n\n \n\n\n\n In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, of SC '13, pages 77:1–77:12, New York, NY, USA, 2013. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ExploringPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Gamell:2013:EPB:2503210.2503303,\n author = {Gamell, Marc and Rodero, Ivan and Parashar, Manish and Bennett, Janine C. and Kolla, Hemanth and Chen, Jacqueline and Bremer, Peer-Timo and Landge, Aaditya G. and Gyulassy, Attila and McCormick, Patrick and Pakin, Scott and Pascucci, Valerio and Klasky, Scott},\n title = {Exploring Power Behaviors and Trade-offs of In-situ Data Analytics},\n booktitle = {Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},\n series = {SC '13},\n year = {2013},\n isbn = {978-1-4503-2378-9},\n location = {Denver, Colorado},\n pages = {77:1--77:12},\n abstract={\n As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.},\n articleno = {77},\n numpages = {12},\n url = {papers/a77-gamell.pdf},\n doi = {10.1145/2503210.2503303},\n acmid = {2503303},\n publisher = {ACM},\n address = {New York, NY, USA},\n} \n\n
\n
\n\n\n
\n As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.\n
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\n  \n 2012\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations.\n \n \n \n \n\n\n \n Landge, A. G; Levine, J. A; Isaacs, K. E; Bhatele, A.; Gamblin, T.; Schulz, M.; Langer, S. H; Bremer, P.; and Pascucci, V.\n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG), 18(12): 2467-2476. Dec 2012.\n (InfoVis'12)\n\n\n\n
\n\n\n\n \n \n \"VisualizingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@ARTICLE{6327252,\nauthor={Aaditya G Landge and Joshua A Levine and Katherine E Isaacs and Abhinav Bhatele and Todd Gamblin and Martin Schulz and Steve H Langer and Peer-Timo Bremer and Valerio Pascucci},\njournal={IEEE Transactions on Visualization and Computer Graphics (TVCG)},\ntitle={Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations},\nyear={2012},\nmonth={Dec},\nvolume={18},\nnumber={12},\npages={2467-2476},\nnote = {(InfoVis'12)},\nabstract={The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system.},\nkeywords={data visualisation;laser beams;mainframes;network topology;parallel processing;physics computing;plasma simulation;plasma-beam interactions;2D view;3D view;IBM Blue Gene-P system;compute nodes;data visualization;hardware interconnect;laser interaction;network structure;network traffic visualization;packet flow;parallel application developers;parallel multiphysics code pF3D;parallel simulation performance;physical network topology;plasma interaction;supercomputers;Computational modeling;Data visualization;Hardware;Layout;Network topology;Performance evaluation;Supercomputers;Performance analysis;network traffic visualization;projected graph layouts},\ndoi={10.1109/TVCG.2012.286},\nurl = {papers/06327252.pdf},\nISSN={1077-2626},}\n\n\n
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\n The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system.\n
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\n \n\n \n \n \n \n \n \n Mapping Applications with Collectives over Sub-communicators on Torus Networks.\n \n \n \n \n\n\n \n Bhatele, A.; Gamblin, T.; Langer, S. H.; Bremer, P.; Draeger, E. W.; Hamann, B.; Isaacs, K. E.; Landge, A. G.; Levine, J. A.; Pascucci, V.; Schulz, M.; and Still, C. H.\n\n\n \n\n\n\n In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, of SC '12, pages 97:1–97:11, Los Alamitos, CA, USA, 2012. IEEE Computer Society Press\n \n\n\n\n
\n\n\n\n \n \n \"MappingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Bhatele:2012:MAC:2388996.2389128,\n author = {Bhatele, Abhinav and Gamblin, Todd and Langer, Steven H. and Bremer, Peer-Timo and Draeger, Erik W. and Hamann, Bernd and Isaacs, Katherine E. and Landge, Aaditya G. and Levine, Joshua A. and Pascucci, Valerio and Schulz, Martin and Still, Charles H.},\n title = {Mapping Applications with Collectives over Sub-communicators on Torus Networks},\n booktitle = {Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},\n series = {SC '12},\n year = {2012},\n isbn = {978-1-4673-0804-5},\n location = {Salt Lake City, Utah},\n pages = {97:1--97:11},\n abstract={The placement of tasks in a parallel application on specific nodes of a supercomputer can significantly impact performance. Traditionally, this task mapping has focused on reducing the distance between communicating tasks on the physical network. This minimizes the number of hops that point-to-point messages travel and thus reduces link sharing between messages and contention. However, for applications that use collectives over sub-communicators, this heuristic may not be optimal. Many collectives can benefit from an increase in bandwidth even at the cost of an increase in hop count, especially when sending large messages. For example, placing communicating tasks in a cube configuration rather than a plane or a line on a torus network increases the number of possible paths messages might take. This increases the available bandwidth which can lead to significant performance gains.\nWe have developed Rubik, a tool that provides a simple and intuitive interface to create a wide variety of mappings for structured communication patterns. Rubik supports a number of elementary operations such as splits, tilts, or shifts, that can be combined into a large number of unique patterns. Each operation can be applied to disjoint groups of processes involved in collectives to increase the effective bandwidth. We demonstrate the use of Rubik for improving performance of two parallel codes, pF3D and Qbox, which use collectives over sub-communicators.},\n articleno = {97},\n numpages = {11},\n url = {papers/bhatele-rubik-mapping-sc12.pdf},\n acmid = {2389128},\n publisher = {IEEE Computer Society Press},\n address = {Los Alamitos, CA, USA},\n}\n\n
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\n The placement of tasks in a parallel application on specific nodes of a supercomputer can significantly impact performance. Traditionally, this task mapping has focused on reducing the distance between communicating tasks on the physical network. This minimizes the number of hops that point-to-point messages travel and thus reduces link sharing between messages and contention. However, for applications that use collectives over sub-communicators, this heuristic may not be optimal. Many collectives can benefit from an increase in bandwidth even at the cost of an increase in hop count, especially when sending large messages. For example, placing communicating tasks in a cube configuration rather than a plane or a line on a torus network increases the number of possible paths messages might take. This increases the available bandwidth which can lead to significant performance gains. We have developed Rubik, a tool that provides a simple and intuitive interface to create a wide variety of mappings for structured communication patterns. Rubik supports a number of elementary operations such as splits, tilts, or shifts, that can be combined into a large number of unique patterns. Each operation can be applied to disjoint groups of processes involved in collectives to increase the effective bandwidth. We demonstrate the use of Rubik for improving performance of two parallel codes, pF3D and Qbox, which use collectives over sub-communicators.\n
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\n \n\n \n \n \n \n \n \n Interactive Linked Visualizations for Performance Analysis of Heterogeneous Computing Clusters.\n \n \n \n \n\n\n \n Landge, A. G.; Levine, J. A.; Isaacs, K. E.; Bhatele, A.; Gamblin, T.; Schulz, M.; Langer, S. H.; Bremer, P.; and Pascucci, V.\n\n\n \n\n\n\n May 2012.\n (Poster)\n\n\n\n
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@misc{landge_poster2,\nauthor = {Aaditya G. Landge and Joshua A. Levine and Katherine E. Isaacs and Abhinav Bhatele and Todd\nGamblin and Martin Schulz and Steve H. Langer and Peer-Timo Bremer and Valerio Pascucci},\njournal={GPU Technology Conference, San Jose, CA},\ntitle = {Interactive Linked Visualizations for Performance Analysis of Heterogeneous Computing\nClusters},\nyear={2012},\nmonth={May},\nvolume={},\nnumber={},\npages={},\nnote = {(Poster)},\nurl={posters/gtc-2012.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploring Performance Data with Boxfish.\n \n \n \n \n\n\n \n Isaacs, K. E.; Landge, A. G.; Gamblin, T.; Bremer, P.; Pascucci, V.; and Hamann, B.\n\n\n \n\n\n\n Nov 2012.\n (Poster)\n\n\n\n
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@misc{kate_poster,\nauthor    = {Katherine E. Isaacs and Aaditya G. Landge and Todd Gamblin and Peer-Timo Bremer and\nValerio Pascucci and Bernd Hamann},\ntitle = {Exploring Performance Data with Boxfish},\njournal = {SC12, Salt Lake City, UT}, year = {2012},\nmonth ={Nov},\nee = {http://doi.ieeecomputersociety.org/10.1109/SC.Companion.2012.202},\nbibsource = {DBLP, http://dblp.uni-trier.de},\nnote= {(Poster)}\n}\n\n
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\n \n\n \n \n \n \n \n \n Performance Visualization of Large Scale Simulations.\n \n \n \n \n\n\n \n Landge, A. G.; Levine, J. A.; Isaacs, K. E.; Bhatele, A.; Gamblin, T.; Schulz, M.; Langer, S. H.; Bremer, P.; and Pascucci, V.\n\n\n \n\n\n\n Nov 2011.\n (BEST POSTER AWARD)\n\n\n\n
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@misc{landge_poster1,\nauthor = {Aaditya G. Landge and Joshua A. Levine and Katherine E.\nIsaacs and Abhinav Bhatele and Todd Gamblin and Martin Schulz and\nSteve H. Langer and Peer-Timo Bremer and Valerio Pascucci},\njournal={SCIx, Scientific Computing and Imaging Institute, Salt Lake\nCity, UT},\ntitle = {Performance Visualization of Large Scale Simulations},\nyear={2011},\nmonth={Nov},\nvolume={},\nnumber={},\npages={},\nnote = {(BEST POSTER AWARD)}, \nurl={posters/SCIx2011.pdf}\n}\n\n
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