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High Performance Computing

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Cover of 'High Performance Computing'

Table of Contents

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    Book Overview
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    Chapter 1 On the Use of Kernel Bypass Mechanisms for High-Performance Inter-container Communications
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    Chapter 2 Continuous-Action Reinforcement Learning for Memory Allocation in Virtualized Servers
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    Chapter 3 Container Orchestration on HPC Clusters
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    Chapter 4 Data Pallets: Containerizing Storage for Reproducibility and Traceability
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    Chapter 5 Sarus: Highly Scalable Docker Containers for HPC Systems
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    Chapter 6 Singularity GPU Containers Execution on HPC Cluster
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    Chapter 7 A Multitenant Container Platform with OKD, Harbor Registry and ELK
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    Chapter 8 Enabling GPU-Enhanced Computer Vision and Machine Learning Research Using Containers
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    Chapter 9 Software and Hardware Co-design for Low-Power HPC Platforms
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    Chapter 10 Modernizing Titan2D, a Parallel AMR Geophysical Flow Code to Support Multiple Rheologies and Extendability
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    Chapter 11 Asynchronous AMR on Multi-GPUs
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    Chapter 12 Batch Solution of Small PDEs with the OPS DSL
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    Chapter 13 Scalable Parallelization of Stencils Using MODA
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    Chapter 14 Comparing High Performance Computing Accelerator Programming Models
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    Chapter 15 Tracking User-Perceived I/O Slowdown via Probing
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    Chapter 16 A Quantitative Approach to Architecting All-Flash Lustre File Systems
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    Chapter 17 MBWU: Benefit Quantification for Data Access Function Offloading
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    Chapter 18 Footprinting Parallel I/O – Machine Learning to Classify Application’s I/O Behavior
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    Chapter 19 Adventures in NoSQL for Metadata Management
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    Chapter 20 Towards High Performance Data Analytics for Climate Change
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    Chapter 21 An Architecture for High Performance Computing and Data Systems Using Byte-Addressable Persistent Memory
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    Chapter 22 Mediating Data Center Storage Diversity in HPC Applications with FAODEL
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    Chapter 23 Predicting File Lifetimes with Machine Learning
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    Chapter 24 An I/O Analysis of HPC Workloads on CephFS and Lustre
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    Chapter 25 Enabling Fast and Highly Effective FPGA Design Process Using the CAPI SNAP Framework
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    Chapter 26 Scaling the Summit: Deploying the World’s Fastest Supercomputer
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    Chapter 27 Parallelware Tools: An Experimental Evaluation on POWER Systems
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    Chapter 28 Performance Evaluation of MPI Libraries on GPU-Enabled OpenPOWER Architectures: Early Experiences
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    Chapter 29 Exploring the Behavior of Coherent Accelerator Processor Interface (CAPI) on IBM Power8+ Architecture and FlashSystem 900
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    Chapter 30 Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer
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    Chapter 31 Performance Comparison for Neuroscience Application Benchmarks
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    Chapter 32 Evaluating POWER Architecture for Distributed Training of Generative Adversarial Networks
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    Chapter 33 A Study on the Performance of Reproducible Computations
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    Chapter 34 Three Numerical Reproducibility Issues That Can Be Explained as Round-Off Error
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    Chapter 35 Training Multiscale-CNN for Large Microscopy Image Classification in One Hour
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    Chapter 36 Benchmarking Deep Learning Infrastructures by Means of TensorFlow and Containers
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    Chapter 37 MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing
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    Chapter 38 Open OnDemand: HPC for Everyone
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    Chapter 39 Highly Interactive, Steered Scientific Workflows on HPC Systems: Optimizing Design Solutions
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    Chapter 40 The Role of Interactive Super-Computing in Using HPC for Urgent Decision Making
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    Chapter 41 Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction
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    Chapter 42 Using FPGAs to Accelerate HPC and Data Analytics on Intel-Based Systems
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    Chapter 43 Exploring the Acceleration of the Met Office NERC Cloud Model Using FPGAs
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    Chapter 44 Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs
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    Chapter 45 In-Situ Data Reduction via Incoherent Sensing
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    Chapter 46 In-Situ Processing in Climate Science
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    Chapter 47 Distributed Out-of-Core Approach for In-Situ Volume Rendering of Massive Dataset
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    Chapter 48 Leveraging NVIDIA Omniverse for In Situ Visualization
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    Chapter 49 Hands-On Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments
Attention for Chapter 43: Exploring the Acceleration of the Met Office NERC Cloud Model Using FPGAs
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