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Extension VM: Interleaved Data Layout in Vector Memory

Overview of attention for article published in ACM Transactions on Architecture and Code Optimization, February 2024
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 383)
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

news
1 news outlet
twitter
3 X users

Readers on

mendeley
2 Mendeley
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Title
Extension VM: Interleaved Data Layout in Vector Memory
Published in
ACM Transactions on Architecture and Code Optimization, February 2024
DOI 10.1145/3631528
Authors

Dunbo Zhang, Qingjie Lang, Ruoxi Wang, Li Shen

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 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 %
Unspecified 1 50%
Professor 1 50%
Lecturer 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Computer Science 1 50%
Engineering 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 06 December 2023.
All research outputs
#3,330,471
of 25,381,783 outputs
Outputs from ACM Transactions on Architecture and Code Optimization
#14
of 383 outputs
Outputs of similar age
#23,279
of 177,928 outputs
Outputs of similar age from ACM Transactions on Architecture and Code Optimization
#1
of 3 outputs
Altmetric has tracked 25,381,783 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 383 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 96% of its peers.
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 177,928 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them