Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence

[1]  M. J. Castro,et al.  Probabilistic tsunami forecasting for early warning , 2021, Nature Communications.

[2]  F. Imamura,et al.  Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks , 2021, Nature Communications.

[3]  M. Ripepe,et al.  Tsunami risk management for crustal earthquakes and non-seismic sources in Italy , 2021, La Rivista del Nuovo Cimento.

[4]  F. Dias,et al.  Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties , 2021, Frontiers in Earth Science.

[5]  Denis Demidov,et al.  AMGCL - A C++ library for efficient solution of large sparse linear systems , 2020, Softw. Impacts.

[6]  Natalja Rakowsky,et al.  Fast Tsunami Simulations for a Real-Time Emergency Response Flow , 2020, 2020 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC).

[7]  I. Mulia,et al.  Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes , 2020, Journal of Geophysical Research: Solid Earth.

[8]  Jorge Ejarque,et al.  Managing Failures in Task-Based Parallel Workflows in Distributed Computing Environments , 2020, Euro-Par.

[9]  Jorge Ejarque,et al.  A Programming Model for Hybrid Workflows: combining Task-based Workflows and Dataflows all-in-one , 2020, Future Gener. Comput. Syst..

[10]  Emanuele Danovaro,et al.  LEXIS Weather and Climate Large-Scale Pilot , 2020, CISIS.

[11]  Jorge Macías Sánchez,et al.  Urgent Tsunami Computing , 2019, 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC).

[12]  Geoffrey Fox,et al.  Understanding ML Driven HPC: Applications and Infrastructure , 2019, 2019 15th International Conference on eScience (eScience).

[13]  Torsten Hoefler,et al.  Reflecting on the Goal and Baseline for Exascale Computing: A Roadmap Based on Weather and Climate Simulations , 2019, Computing in Science & Engineering.

[14]  Denis Demidov,et al.  AMGCL: An Efficient, Flexible, and Extensible Algebraic Multigrid Implementation , 2018, Lobachevskii Journal of Mathematics.

[15]  Hilary James Oliver,et al.  Cylc: A Workflow Engine for Cycling Systems , 2018, J. Open Source Softw..

[16]  Domenico Talia,et al.  A Workflow Management System for Scalable Data Mining on Clouds , 2018, IEEE Transactions on Services Computing.

[17]  Thomas Ludwig,et al.  Survey of Storage Systems for High-Performance Computing , 2018, Supercomput. Front. Innov..

[18]  Rajkumar Buyya,et al.  Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms , 2018, Future Gener. Comput. Syst..

[19]  Quincey Koziol,et al.  DAOS for Extreme-scale Systems in Scientific Applications , 2017, ArXiv.

[20]  Toni Cortes,et al.  Dataclay: A distributed data store for effective inter-player data sharing , 2017, J. Syst. Softw..

[21]  Vanessa Sochat,et al.  Singularity: Scientific containers for mobility of compute , 2017, PloS one.

[22]  F Dias,et al.  Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[23]  Jordi Torres,et al.  PyCOMPSs: Parallel computational workflows in Python , 2016, Int. J. High Perform. Comput. Appl..

[24]  John Chilton,et al.  The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update , 2016, Nucleic Acids Res..

[25]  Luís Veiga,et al.  WaaS: Workflow-as-a-Service for the Cloud with Scheduling of Continuous and Data-Intensive Workflows , 2016, Comput. J..

[26]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[27]  Jorge Ejarque,et al.  COMP Superscalar, an interoperable programming framework , 2015 .

[28]  Bronis R. de Supinski,et al.  The Spack package manager: bringing order to HPC software chaos , 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

[29]  Marta Mattoso,et al.  Dynamic steering of HPC scientific workflows: A survey , 2015, Future Gener. Comput. Syst..

[30]  Eric S. Chung,et al.  A reconfigurable fabric for accelerating large-scale datacenter services , 2014, 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA).

[31]  Dana Petcu,et al.  Exascale Machines Require New Programming Paradigms and Runtimes , 2015, Supercomput. Front. Innov..

[32]  Ming Zhao,et al.  Tracking Scheme Dependence of Simulated Tropical Cyclone Response to Idealized Climate Simulations , 2014 .

[33]  Marian Bubak,et al.  Applying workflow as a service paradigm to application farming , 2014, Concurr. Comput. Pract. Exp..

[34]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[35]  Rosa M. Badia,et al.  ServiceSs: An Interoperable Programming Framework for the Cloud , 2014, Journal of Grid Computing.

[36]  Riccardo Rossi,et al.  Migration of a generic multi-physics framework to HPC environments , 2013 .

[37]  Bingsheng He,et al.  Monetary Cost Optimizations for Hosting Workflow-as-a-Service in IaaS Clouds , 2013, IEEE Transactions on Cloud Computing.

[38]  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..

[39]  Domenico Talia,et al.  Workflow Systems for Science: Concepts and Tools , 2013 .

[40]  Andy Georges,et al.  EasyBuild: Building Software with Ease , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[41]  Daniel S. Katz,et al.  Swift: A language for distributed parallel scripting , 2011, Parallel Comput..

[42]  S. Gualdi,et al.  Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model , 2011 .

[43]  Gerd Heber,et al.  An overview of the HDF5 technology suite and its applications , 2011, AD '11.

[44]  A. Nekrutenko,et al.  Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences , 2010, Genome Biology.

[45]  Eugenio Oñate,et al.  An Object-oriented Environment for Developing Finite Element Codes for Multi-disciplinary Applications , 2010 .

[46]  Carole A. Goble,et al.  Scientific Workflows as Services in caGrid: A Taverna and gRAVI Approach , 2009, 2009 IEEE International Conference on Web Services.

[47]  Jun Qin,et al.  ASKALON: a Grid application development and computing environment , 2005, The 6th IEEE/ACM International Workshop on Grid Computing, 2005..

[48]  Daniel S. Katz,et al.  Pegasus: A framework for mapping complex scientific workflows onto distributed systems , 2005, Sci. Program..

[49]  Bertram Ludäscher,et al.  Kepler: an extensible system for design and execution of scientific workflows , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[50]  David F. Snelling,et al.  UNICORE—a Grid computing environment , 2002, Concurr. Comput. Pract. Exp..

[51]  John D. Davis,et al.  The MareNostrum Experimental Exascale Platform (MEEP) , 2021, Supercomput. Front. Innov..

[52]  Sandro Fiore,et al.  Towards HPC and Big Data Analytics Convergence: Design and Experimental Evaluation of a HPDA Framework for eScience at Scale , 2021, IEEE Access.

[53]  Alexandra Fedorova,et al.  Processing in Storage Class Memory , 2020, HotStorage.

[54]  Christina Freytag,et al.  Using Mpi Portable Parallel Programming With The Message Passing Interface , 2016 .

[55]  Simon Moser,et al.  Topology and Orchestration Specification for Cloud Applications Version 1.0 , 2013 .

[56]  Hamid Laga,et al.  CUDA (Computer Unified Device Architecture) , 2009 .

[57]  Sam Ruby,et al.  RESTful Web Services , 2007 .

[58]  A. Kivity,et al.  kvm : the Linux Virtual Machine Monitor , 2007 .

[59]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .