Name
Abstract | ||
|---|---|---|
Emerging data-intensive applications attempt to process and provide insight into vast amounts of online data. A new class of linear algebra algorithms can efficiently execute sparse matrix-matrix and matrix-vector multiplications on large-scale, shared memory multiprocessor systems, enabling analysts to more easily discern meaningful data relationships, such as those in social networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MC.2015.228 | Computer |
Keywords | Field | DocType |
Data-intensive applications ,Memory management,Concurrent programming,Software engineering,Data analysis,Multiprocessors,Sparse matrices | Linear algebra,Social network,Computer science,Graph analytics,Theoretical computer science,Memory management,Software,Shared memory multiprocessor,Concurrent computing,Sparse matrix | Journal |
Volume | Issue | ISSN |
48 | 8 | 0018-9162 |
Citations | PageRank | References |
5 | 0.56 | 13 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Buono | 1 | 39 | 6.52 |
| John A. Gunnels | 2 | 717 | 83.20 |
| Xinyu Que | 3 | 124 | 11.81 |
| Fabio Checconi | 4 | 197 | 14.03 |
| Fabrizio Petrini | 5 | 2050 | 165.82 |
| Tai-Ching Tuan | 6 | 41 | 6.15 |
| Chris Long | 7 | 14 | 1.52 |