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High Performance Computing

Overview of attention for book
Cover of 'High Performance Computing'

Table of Contents

  1. Altmetric Badge
    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 44: Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs
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Chapter title
Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs
Chapter number 44
Book title
High Performance Computing
Published by
Springer, Cham, December 2019
DOI 10.1007/978-3-030-34356-9_44
Book ISBNs
978-3-03-034355-2, 978-3-03-034356-9

Chao Jiang, Dave Ojika, Thorsten Kurth, Prabhat, Sofia Vallecorsa, Bhavesh Patel, Herman Lam, Jiang, Chao, Ojika, Dave, Kurth, Thorsten, Prabhat, , Vallecorsa, Sofia, Patel, Bhavesh, Lam, Herman

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 18%
Researcher 2 18%
Student > Bachelor 1 9%
Other 1 9%
Student > Ph. D. Student 1 9%
Other 1 9%
Unknown 3 27%
Readers by discipline Count As %
Computer Science 3 27%
Engineering 2 18%
Business, Management and Accounting 1 9%
Physics and Astronomy 1 9%
Mathematics 1 9%
Other 0 0%
Unknown 3 27%