High performance computing : ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers /: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers. (2019)

• Record Type: Book
• Further Information: Note: Michèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode, editors.
• Editors: Weiland, Michèle; Juckeland, Guido; Alam, Sadaf; Jagode, Heike
• Other Names: ISC High Performance (Conference), 34th
• Contents: On the Use of Kernel Bypass Mechanisms for High-Performance Inter-container Communications -- Continuous-Action Reinforcement Learning for Memory Allocation in Virtualized Servers -- Container Orchestration on HPC Clusters -- Data Pallets: Containerizing Storage for Reproducibility and Traceability -- Sarus: Highly Scalable Docker Containers for HPC Systems -- Singularity GPU Containers Execution on HPC Cluster -- A Multitenant Container Platform with OKD, Harbor Registry and ELK -- Enabling GPU-Enhanced Computer Vision and Machine Learning Research Using Containers -- Software and Hardware Co-design for Low-Power HPC Platforms -- Modernizing Titan2D, a Parallel AMR Geophysical Flow Code to Support Multiple Rheologies and Extendability -- Asynchronous AMR on Multi-GPUs -- Batch Solution of Small PDEs with the OPS DSL -- Scalable Parallelization of Stencils Using MODA -- Comparing High Performance Computing Accelerator Programming Models -- Tracking User-Perceived I/O Slowdown via Probing -- A Quantitative Approach to Architecting All-Flash Lustre File Systems -- MBWU: Benefit Quantification for Data Access Function Offloading -- Footprinting Parallel I/O -- Machine Learning to Classify Application's I/O Behavior -- Adventures in NoSQL for Metadata Management -- Towards High Performance Data Analytics for Climate Change -- An Architecture for High Performance Computing and Data Systems Using Byte-Addressable Persistent Memory -- Mediating Data Center Storage Diversity in HPC Applications with FAODEL -- Predicting File Lifetimes with Machine Learning -- An I/O Analysis of HPC Workloads on CephFS and Lustre -- Enabling Fast and Highly Effective FPGA Design Process Using the CAPI SNAP Framework -- Scaling the Summit: Deploying the World's Fastest Supercomputer -- Parallelware Tools: An Experimental Evaluation on POWER Systems -- Performance Evaluation of MPI Libraries on GPU-Enabled OpenPOWER Architecture: Early Experiences Exploring the Behavior of Coherent Accelerator Processor Interface (CAPI) on IBM Power8+ Architecture and FlashSystem 900 -- Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer -- Performance Comparison for Neuroscience Application Benchmarks -- Evaluating POWER Architecture for Distributed Training of Generative Adversarial Networks -- A Study on the Performance of Reproducible Computations -- Three Numerical Reproducibility Issues That Can Be Explained as Round-Off Error -- Training Multiscale-CNN for Large Microscopy Image Classification in One Hour -- Benchmarking Deep Learning Infrastructures by Means of TensorFlow and Containers -- MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing -- Open OnDemand: HPC for Everyone -- Highly Interactive, Steered Scientific Workflows on HPC Systems: Optimizing Design Solutions -- The Role of Interactive Super-Computing in Using HPC for Urgent Decision Making -- Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction -- Using FPGAs to Accelerate HPC and Data Analytics on Intel-Based Systems -- Exploring the Acceleration of the Met Office NERC Cloud Model Using FPGAs -- Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs -- In-Situ Data Reduction via Incoherent Sensing -- In-Situ Processing in Climate Science -- Distributed Out-of-Core Approach for In-Situ Volume Rendering of Massive Dataset -- Leveraging NVIDIA Omniverse for In Situ Visualization -- Hands-On Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource (xxv, 659 pages), illustrations
• Subjects: 004.1/1; High performance computing -- Congresses; Supercomputers -- Congresses
• Languages: English
• ISBNs: 9783030343569; 3030343561
• Related ISBNs: 9783030343552
• Notes: Note: Includes bibliographical references and author index.; Note: Description based on online resource (SpringerLink, viewed January 24, 2020).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.