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Languages and Compilers for Parallel Computing

Overview of attention for book
Cover of 'Languages and Compilers for Parallel Computing'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Just in Time Load Balancing
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    Chapter 2 AlphaZ: A System for Design Space Exploration in the Polyhedral Model
  4. Altmetric Badge
    Chapter 3 Compiler Optimizations: Machine Learning versus O3
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    Chapter 4 The STAPL Parallel Graph Library
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    Chapter 5 Set and Relation Manipulation for the Sparse Polyhedral Framework
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    Chapter 6 Parallel Clustered Low-Rank Approximation of Graphs and Its Application to Link Prediction
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    Chapter 7 OmpSs-OpenCL Programming Model for Heterogeneous Systems
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    Chapter 8 Compiler Optimizations for Industrial Unstructured Mesh CFD Applications on GPUs
  10. Altmetric Badge
    Chapter 9 Languages and Compilers for Parallel Computing
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    Chapter 10 A Study on the Impact of Compiler Optimizations on High-Level Synthesis
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    Chapter 11 FlowPools: A Lock-Free Deterministic Concurrent Dataflow Abstraction
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    Chapter 12 Task Parallelism and Data Distribution: An Overview of Explicit Parallel Programming Languages
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    Chapter 13 A Fast Parallel Graph Partitioner for Shared-Memory Inspector/Executor Strategies
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    Chapter 14 A Software-Based Method-Level Speculation Framework for the Java Platform
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    Chapter 15 Ant: A Debugging Framework for MPI Parallel Programs
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    Chapter 16 Compiler Automatic Discovery of OmpSs Task Dependencies
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    Chapter 17 Beyond Do Loops: Data Transfer Generation with Convex Array Regions
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    Chapter 18 Finish Accumulators: An Efficient Reduction Construct for Dynamic Task Parallelism
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    Chapter 19 FlashbackSTM: Improving STM Performance by Remembering the Past
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    Chapter 20 Kaira: Generating Parallel Libraries and Their Usage with Octave
  22. Altmetric Badge
    Chapter 21 Language and Architecture Independent Software Thread-Level Speculation
  23. Altmetric Badge
    Chapter 22 Abstractions for Defining Semi-Regular Grids Orthogonally from Stencils
Attention for Chapter 3: Compiler Optimizations: Machine Learning versus O3
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3 X users

Citations

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Chapter title
Compiler Optimizations: Machine Learning versus O3
Chapter number 3
Book title
Languages and Compilers for Parallel Computing
Published in
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-37658-0_3
Book ISBNs
978-3-64-237657-3, 978-3-64-237658-0
Authors

Yuriy Kashnikov, Jean Christophe Beyler, William Jalby, Kashnikov, Yuriy, Beyler, Jean Christophe, Jalby, William

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 71%
Unspecified 1 14%
Researcher 1 14%
Readers by discipline Count As %
Computer Science 6 86%
Unspecified 1 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 August 2013.
All research outputs
#14,174,202
of 22,716,996 outputs
Outputs from Lecture notes in computer science
#4,306
of 8,124 outputs
Outputs of similar age
#167,544
of 280,757 outputs
Outputs of similar age from Lecture notes in computer science
#167
of 314 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,757 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 314 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.