The future of scientific workflows
暂无分享,去创建一个
Lavanya Ramakrishnan | Christopher D. Carothers | Jeffrey S. Vetter | Kerstin Kleese van Dam | Ilkay Altintas | Manish Parashar | Ewa Deelman | Kenneth Moreland | Tom Peterka | Michela Taufer | J. Vetter | I. Altintas | M. Parashar | M. Taufer | E. Deelman | K. Moreland | C. Carothers | T. Peterka | K. K. Dam | L. Ramakrishnan
[1] Frank Leymann,et al. Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging, and More , 2005 .
[2] Bertram Ludäscher,et al. Modeling and Querying Scientific Workflow Provenance in the D-OPM , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.
[3] Ian Foster,et al. WGL – A Workflow Generator Language and Utility , 2013 .
[4] Karsten Schwan,et al. Flexpath: Type-Based Publish/Subscribe System for Large-Scale Science Analytics , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.
[5] Shiyong Lu,et al. Storing, reasoning, and querying OPM-compliant scientific workflow provenance using relational databases , 2011, Future Gener. Comput. Syst..
[6] Ganesh Gopalakrishnan,et al. Determinism and Reproducibility in Large-Scale HPC Systems , 2013 .
[7] Brian Tarran,et al. A failure of prediction? , 2016 .
[8] Scott Klasky,et al. Moving the Code to the Data - Dynamic Code Deployment Using ActiveSpaces , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.
[9] Torsten Hoefler,et al. Designing Bit-Reproducible Portable High-Performance Applications , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.
[10] Marta Mattoso,et al. A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds , 2012, Journal of Grid Computing.
[11] Irwin D. Kuntz,et al. Development and validation of a modular, extensible docking program: DOCK 5 , 2006, J. Comput. Aided Mol. Des..
[12] Schahram Dustdar,et al. Performance metrics and ontologies for Grid workflows , 2007, Future Gener. Comput. Syst..
[13] Yolanda Gil,et al. Pegasus: Mapping Scientific Workflows onto the Grid , 2004, European Across Grids Conference.
[14] E.R. Mark,et al. Enhancements to the eXtensible Data Model and Format (XDMF) , 2007, 2007 DoD High Performance Computing Modernization Program Users Group Conference.
[15] Scott Klasky,et al. Understanding I/O Performance Using I/O Skeletal Applications , 2012, Euro-Par.
[16] Scott Klasky,et al. ADIOS Visualization Schema: A First Step Towards Improving Interdisciplinary Collaboration in High Performance Computing , 2013, 2013 IEEE 9th International Conference on e-Science.
[17] Pavan Balaji,et al. On the Reproducibility of MPI Reduction Operations , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.
[18] Al Shaffer. 2009 High Performance Computing Modernization Program Users Group Conference , 2009 .
[19] Yogesh L. Simmhan,et al. The Trident Scientific Workflow Workbench , 2008, 2008 IEEE Fourth International Conference on eScience.
[20] Manish Parashar,et al. Flexible Scheduling and Control of Bandwidth and In-transit Services for End-to-End Application Workflows , 2014, 2014 Fourth International Workshop on Network-Aware Data Management.
[21] Carole A. Goble,et al. The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud , 2013, Nucleic Acids Res..
[22] Karsten Schwan,et al. I/O Containers: Managing the Data Analytics and Visualization Pipelines of High End Codes , 2013, 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum.
[23] Philip Saponaro,et al. Improving numerical reproducibility and stability in large-scale numerical simulations on GPUs , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).
[24] Daniel S. Katz,et al. Pegasus: A framework for mapping complex scientific workflows onto distributed systems , 2005, Sci. Program..
[25] Michael Stonebraker,et al. The Architecture of SciDB , 2011, SSDBM.
[26] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[27] Judy Qiu,et al. A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures , 2014, 2014 IEEE International Congress on Big Data.
[28] Justin M. Wozniak,et al. Lessons Learned from Building In Situ Coupling Frameworks , 2015, ISAV@SC.
[29] Michael A. Heroux,et al. Toward Local Failure Local Recovery Resilience Model using MPI-ULFM , 2014, EuroMPI/ASIA.
[30] Keita Teranishi,et al. Extreme-Scale Viability of Collective Communication for Resilient Task Scheduling and Work Stealing , 2014, 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks.
[31] Lavanya Ramakrishnan,et al. Combining Workflow Templates with a Shared Space-Based Execution Model , 2014, 2014 9th Workshop on Workflows in Support of Large-Scale Science.
[32] Cláudio T. Silva,et al. Querying and re-using workflows with VsTrails , 2008, SIGMOD Conference.
[33] John L. Gustafson,et al. The End of Error: Unum Computing , 2015 .
[34] Karsten Schwan,et al. PreDatA – preparatory data analytics on peta-scale machines , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).
[35] Daniel S. Katz,et al. Swift: A language for distributed parallel scripting , 2011, Parallel Comput..
[36] Suresh Narayanan,et al. Effective End-to-end Management of Data Acquisition and Analysis for X-ray Photon Correlation Spectroscopy , 2013 .
[37] Jason Maassen,et al. Programming Scientific and Distributed Workflow with Triana Services , 2004 .
[38] Ian Taylor,et al. Programming scientific and distributed workflow with Triana services: Research Articles , 2006 .
[39] H. L.,et al. Van Nostrand's Scientific Encyclopedia , 1938, Nature.
[40] Bertram Ludäscher,et al. Kepler: an extensible system for design and execution of scientific workflows , 2004 .
[41] Karsten Schwan,et al. Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS) , 2008, CLADE '08.
[42] James Demmel,et al. Parallel Reproducible Summation , 2015, IEEE Transactions on Computers.
[43] Sriram Krishnamoorthy,et al. Enabling Structured Exploration of Workflow Performance Variability in Extreme-Scale Environments , 2015 .
[44] Matthieu Dreher,et al. Bredala: Semantic Data Redistribution for In Situ Applications , 2016, 2016 IEEE International Conference on Cluster Computing (CLUSTER).
[45] Miron Livny,et al. Pegasus, a workflow management system for science automation , 2015, Future Gener. Comput. Syst..
[46] Rick Cattell,et al. Scalable SQL and NoSQL data stores , 2011, SGMD.
[47] Fan Zhang,et al. Combining in-situ and in-transit processing to enable extreme-scale scientific analysis , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.
[48] Kenneth D. Moreland. The Future of Scientific Workflows. Report of the DOE NGNS/CS Scientific Workflows Workshop (Sandia contributions) , 2015 .
[49] Daniel S. Katz,et al. Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking , 2009, Int. J. Comput. Sci. Eng..
[50] Sergey Brin,et al. The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.
[51] Daniel J. Blankenberg,et al. Galaxy: a platform for interactive large-scale genome analysis. , 2005, Genome research.
[52] Alex,et al. VizSchema - a Unified Visualization of Computational Accelerator Physics Data , 2010 .
[53] Zhao Zhang,et al. Parallel Scripting for Applications at the Petascale and Beyond , 2009, Computer.
[54] Hal Finkel,et al. HACC: Simulating Sky Surveys on State-of-the-Art Supercomputing Architectures , 2014, 1410.2805.
[55] C.R. Johnson,et al. SCIRun: A Scientific Programming Environment for Computational Steering , 1995, Proceedings of the IEEE/ACM SC95 Conference.
[56] Viraj N. Bhat,et al. Autonomic management of data streaming and in-transit processing for data intensive scientific workflows , 2008 .
[57] Yolanda Gil,et al. Enhancing reproducibility for computational methods , 2016, Science.
[58] Douglas Thain,et al. Weaver: integrating distributed computing abstractions into scientific workflows using Python , 2010, HPDC '10.
[59] William Kahan,et al. Pracniques: further remarks on reducing truncation errors , 1965, CACM.
[60] Edward A. Lee,et al. CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2000; 00:1–7 Prepared using cpeauth.cls [Version: 2002/09/19 v2.02] Taverna: Lessons in creating , 2022 .
[61] David R. Mathog,et al. Parallel BLAST on split databases , 2003, Bioinform..
[62] David H. Bailey,et al. High-precision floating-point arithmetic in scientific computation , 2004, Computing in Science & Engineering.
[63] Ewa Deelman,et al. Failure prediction and localization in large scientific workflows , 2011, WORKS '11.
[64] Karsten Schwan,et al. DataStager: scalable data staging services for petascale applications , 2009, HPDC '09.
[65] Robert B. Ross,et al. High-Performance Parallel I/O , 2006, PVM/MPI.
[66] Karsten Schwan,et al. In-situ I/O processing: a case for location flexibility , 2011, PDSW '11.
[67] Scott Klasky,et al. Exploring Automatic, Online Failure Recovery for Scientific Applications at Extreme Scales , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.
[68] Aart J. C. Bik,et al. Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.
[69] Mark Greenwood,et al. Taverna: lessons in creating a workflow environment for the life sciences: Research Articles , 2006 .
[70] Cláudio T. Silva,et al. Provenance for Visualizations: Reproducibility and Beyond , 2007, Computing in Science & Engineering.
[71] Geoffrey C. Fox,et al. MapReduce for Data Intensive Scientific Analyses , 2008, 2008 IEEE Fourth International Conference on eScience.
[72] Daniel S. Katz,et al. Using Application Skeletons to Improve eScience Infrastructure , 2014, 2014 IEEE 10th International Conference on e-Science.
[73] Yogesh L. Simmhan,et al. The Open Provenance Model core specification (v1.1) , 2011, Future Gener. Comput. Syst..
[74] Franck Cappello,et al. Fault Tolerance in Petascale/ Exascale Systems: Current Knowledge, Challenges and Research Opportunities , 2009, Int. J. High Perform. Comput. Appl..
[75] Cláudio T. Silva,et al. VisTrails: enabling interactive multiple-view visualizations , 2005, VIS 05. IEEE Visualization, 2005..
[76] Ann L. Chervenak,et al. Characterizing and profiling scientific workflows , 2013, Future Gener. Comput. Syst..
[77] OEG-DIA. Towards Open Publication of Reusable Scientific Workflows : Abstractions , Standards and Linked Data , 2012 .
[78] Andrew Thall. Extended-precision floating-point numbers for GPU computation , 2006, SIGGRAPH '06.
[79] Daniel S. Katz,et al. Reusability in Science: From Initial User Engagement to Dissemination of Results , 2013, ArXiv.
[80] Michela Taufer,et al. On the Need for Reproducible Numerical Accuracy through Intelligent Runtime Selection of Reduction Algorithms at the Extreme Scale , 2015, 2015 IEEE International Conference on Cluster Computing.
[81] Andrey Gubarev,et al. Dremel : Interactive Analysis of Web-Scale Datasets , 2011 .
[82] A. Nekrutenko,et al. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences , 2010, Genome Biology.
[83] Ken Martin,et al. Time Dependent Processing in a Parallel Pipeline Architecture , 2007, IEEE Transactions on Visualization and Computer Graphics.
[84] Nathan D. Price,et al. Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models , 2014, PLoS Comput. Biol..
[85] Gregor von Laszewski,et al. Swift: Fast, Reliable, Loosely Coupled Parallel Computation , 2007, 2007 IEEE Congress on Services (Services 2007).
[86] Lars Koesterke,et al. PerfExpert: An Easy-to-Use Performance Diagnosis Tool for HPC Applications , 2010, 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis.
[87] Ralph Bergmann,et al. Similarity assessment and efficient retrieval of semantic workflows , 2014, Inf. Syst..
[88] David H. Laidlaw,et al. The application visualization system: a computational environment for scientific visualization , 1989, IEEE Computer Graphics and Applications.
[89] Bohn Stafleu van Loghum,et al. Online … , 2002, LOG IN.
[90] Li Zhao,et al. SCEC CyberShake Workflows - Automating Probabilistic Seismic Hazard Analysis Calculations , 2007, Workflows for e-Science, Scientific Workflows for Grids.
[91] Rajkumar Buyya,et al. Multiobjective differential evolution for scheduling workflow applications on global Grids , 2009, Concurr. Comput. Pract. Exp..
[92] C. Kesselman,et al. CyberShake: A Physics-Based Seismic Hazard Model for Southern California , 2011 .
[93] Scott Klasky,et al. Experiments with in-transit processing for data intensive grid workflows , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.
[94] Valerie Hendrix,et al. Experiences with User-Centered Design for the Tigres Workflow API , 2014, 2014 IEEE 10th International Conference on e-Science.
[95] Margo I. Seltzer,et al. Layering in Provenance Systems , 2009, USENIX Annual Technical Conference.
[96] Scott Klasky,et al. DataSpaces: an interaction and coordination framework for coupled simulation workflows , 2012, HPDC '10.