Name
Abstract | ||
|---|---|---|
R is a widely-used statistical programming language in the data science community. However, in the big data era, R faces the challenges from large scale data analysis tasks. It lacks the ability of distributed linear algebra computation in its local interactive shell. In this paper, we propose iPLAR, a system that runs in the interactive R environment, wraps the high performance parallel linear algebra library, and provides a group of easy-to-use interfaces. iPLAR adopts the client-server model to uncouple the interactive shell from the ScaLAPACK/MPI distributed computing backend. In addition, it provides R users with a group of parallel-detail-transparent interfaces that are similar to the native R linear algebra interfaces. We evaluate the efficiency of iPLAR with representative basic matrix operations and two widely-used machine learning algorithms. Experimental results show that iPLAR achieves the near-linear data scalability and enhances the interactive processing capability of R to large problem scales. |
Year | Venue | Field |
|---|---|---|
2015 | ICA3PP | Linear algebra,Computer science,Parallel computing,ScaLAPACK,Interactive programming,Matrix multiplication,Big data,Scalability,Computation |
DocType | Citations | PageRank |
Conference | 1 | 0.43 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaokang Wang | 1 | 17 | 5.17 |
| Shiqing Fan | 2 | 1 | 1.45 |
| Rong Gu | 3 | 110 | 17.77 |
| Chunfeng Yuan | 4 | 17 | 2.90 |
| Huang, Yihua | 5 | 167 | 22.07 |