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Advanced Parallel Processing Technologies

Overview of attention for book
Advanced Parallel Processing Technologies
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 RV-CNN: Flexible and Efficient Instruction Set for CNNs Based on RISC-V Processors
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    Chapter 2 Compiling Optimization for Neural Network Accelerators
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    Chapter 3 ZhuQue: A Neural Network Programming Model Based on Labeled Data Layout
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    Chapter 4 Reducing Rename Overhead in Full-Path-Indexed File System
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    Chapter 5 Partition and Scheduling Algorithms for Neural Network Accelerators
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    Chapter 6 SPART: Optimizing CNNs by Utilizing Both Sparsity of Weights and Feature Maps
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    Chapter 7 DA-BERT: Enhancing Part-of-Speech Tagging of Aspect Sentiment Analysis Using BERT
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    Chapter 8 Random Inception Module and Its Parallel Implementation
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    Chapter 9 CBA-Detector: An Accurate Detector Against Cache-Based Attacks Using HPCs and Pintools
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    Chapter 10 An Efficient Log Parsing Algorithm Based on Heuristic Rules
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    Chapter 11 Distribution Forest: An Anomaly Detection Method Based on Isolation Forest
Attention for Chapter 4: Reducing Rename Overhead in Full-Path-Indexed File System
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Chapter title
Reducing Rename Overhead in Full-Path-Indexed File System
Chapter number 4
Book title
Advanced Parallel Processing Technologies
Published by
Springer, Cham, August 2019
DOI 10.1007/978-3-030-29611-7_4
Book ISBNs
978-3-03-029610-0, 978-3-03-029611-7
Authors

Longhua Wang, Youyou Lu, Siyang Li, Fan Yang, Jiwu Shu, Wang, Longhua, Lu, Youyou, Li, Siyang, Yang, Fan, Shu, Jiwu

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Student > Bachelor 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Economics, Econometrics and Finance 1 50%

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