Heike Jagode(editor)
Heike Jagode is a Research Assistant Professor in the Innovative Computing Laboratory (ICL), and an Adjunct Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS), in the Tickle College of Engineering, at the University of Tennessee - Knoxville (UTK). She specializes in High Performance Computing (HPC) and the efficient use of advanced computer architectures; focusing primarily on developing methods and tools for performance analysis, tuning, and efficient energy use of parallel scientific applications. Heike's Ph.D. research focused on a multi-disciplinary effort to develop Dataflow Programming Paradigms for Computational Chemistry Methods to make these methods compatible with next-generation task scheduling systems like StarPU or PaRSEC. In her research, she converted the state-of-the-art NWChem Coupled Cluster methods into different dataflow versions and demonstrated the benefits of dataflow-based task execution over coarse grain parallelism in terms of scalability, resource utilization, and programmability. Education: Doctor of Philosophy (Ph.D.) in Computer Science, University of Tennessee, Knoxville, USA Master of Science (M.Sc.) in High Performance Computing, University of Edinburgh, EPCC, Scotland, UK Master of Science (M.Sc.) in Applied Techno-Mathematics, University of Applied Sciences Mittweida, Germany Bachelor of Science (B.Sc.) in Applied Mathematics, University of Applied Sciences Mittweida, Germany Heike Jagode is a Research Assistant Professor in the Innovative Computing Laboratory (ICL), and an Adjunct Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS), in the Tickle College of Engineering, at the University of Tennessee - Knoxville (UTK). She specializes in High Performance Computing (HPC) and the efficient use of advanced computer architectures; focusing primarily on developing methods and tools for performance analysis, tuning, and efficient energy use of parallel scientific applications. Heike's Ph.D. research focused on a multi-disciplinary effort to develop Dataflow Programming Paradigms for Computational Chemistry Methods to make these methods compatible with next-generation task scheduling systems like StarPU or PaRSEC. In her research, she converted the state-of-the-art NWChem Coupled Cluster methods into different dataflow versions and demonstrated the benefits of dataflow-based task execution over coarse grain parallelism in terms of scalability, resource utilization, and programmability. Education: Doctor of Philosophy (Ph.D.) in Computer Science, University of Tennessee, Knoxville, USA Master of Science (M.Sc.) in High Performance Computing, University of Edinburgh, EPCC, Scotland, UK Master of Science (M.Sc.) in Applied Techno-Mathematics, University of Applied Sciences Mittweida, Germany Bachelor of Science (B.Sc.) in Applied Mathematics, University of Applied Sciences Mittweida, Germany
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Heike Jagode is a Research Assistant Professor in the Innovative Computing Laboratory (ICL), and an Adjunct Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS), in the Tickle College of Engineering, at the University of Tennessee - Knoxville (UTK). She specializes in High Performance Computing (HPC) and the efficient use of advanced computer architectures; focusing primarily on developing methods and tools for performance analysis, tuning, and efficient energy use of parallel scientific applications. Heike's Ph.D. research focused on a multi-disciplinary effort to develop Dataflow Programming Paradigms for Computational Chemistry Methods to make these methods compatible with next-generation task scheduling systems like StarPU or PaRSEC. In her research, she converted the state-of-the-art NWChem Coupled Cluster methods into different dataflow versions and demonstrated the benefits of dataflow-based task execution over coarse grain parallelism in terms of scalability, resource utilization, and programmability. Education: Doctor of Philosophy (Ph.D.) in Computer Science, University of Tennessee, Knoxville, USA Master of Science (M.Sc.) in High Performance Computing, University of Edinburgh, EPCC, Scotland, UK Master of Science (M.Sc.) in Applied Techno-Mathematics, University of Applied Sciences Mittweida, Germany Bachelor of Science (B.Sc.) in Applied Mathematics, University of Applied Sciences Mittweida, Germany
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High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers
This book constitutes the refereed post-conference proceedings of 13 workshops held at the 34th International ISC High Performance 2019 Conference, in Frankfurt, Germany, in June 2019: HPC I/O in the Data Center (HPC-IODC), Workshop on Performance & Scalability of Storage Systems (WOPSSS), Workshop on Performance & Scalability of Storage Systems (WOPSSS), 13th Workshop on Virtualization in High-Performance Cloud Computing (VHPC '18), 3rd International Workshop on In Situ Visualization: Introduction and Applications, ExaComm: Fourth International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, International Workshop on OpenPOWER for HPC (IWOPH18), IXPUG Workshop: Many-core Computing on Intel, Processors: Applications, Performance and Best-Practice Solutions, Workshop on Sustainable Ultrascale Computing Systems, Approximate and Transprecision Computing on Emerging Technologies (ATCET), First Workshop on the Convergence of Large Scale Simulation and Artificial Intelligence, 3rd Workshop for Open Source Supercomputing (OpenSuCo), First Workshop on Interactive High-Performance Computing, Workshop on Performance Portable Programming Models for Accelerators (P^3MA). The 48 full papers included in this volume were carefully reviewed and selected. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include HPC computer architecture and hardware; programming models, system software, and applications; solutions for heterogeneity, reliability, power efficiency of systems; virtualization and containerized environments; big data and cloud computing; and artificial intelligence.
