| Title |
Characterizing Scalability of Sparse Matrix–Vector Multiplications on Phytium FT-2000+
|
|---|---|
| Published in |
International Journal of Parallel Programming, November 2019
|
| DOI | 10.1007/s10766-019-00646-x |
| Authors |
Donglin Chen, Jianbin Fang, Chuanfu Xu, Shizhao Chen, Zheng Wang |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Nepal | 1 | 8% |
| Italy | 1 | 8% |
| United States | 1 | 8% |
| United Kingdom | 1 | 8% |
| Unknown | 8 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 6 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 17% |
| Lecturer | 1 | 17% |
| Student > Master | 1 | 17% |
| Unknown | 3 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 50% |
| Unknown | 3 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 23 December 2020.
All research outputs
#5,706,099
of 23,508,125 outputs
Outputs from International Journal of Parallel Programming
#28
of 172 outputs
Outputs of similar age
#100,998
of 359,933 outputs
Outputs of similar age from International Journal of Parallel Programming
#1
of 2 outputs
Altmetric has tracked 23,508,125 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 172 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 83% 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 359,933 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 2 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