Name
Abstract | ||
|---|---|---|
Even though in recent years the scale of statistical analysis problems has increased tremendously, many statistical software tools are still limited to single-node computations. However, statistical analyses are largely based on dense linear algebra operations, which have been deeply studied, optimized and parallelized in the high-performance-computing community. To make high-performance distributed computations available for statistical analysis, and thus enable large scale statistical computations, we introduce RElem, an open source package that integrates the distributed dense linear algebra library Elemental into R. While on the one hand, RElem provides direct wrappers of Elementalu0027s routines, on the other hand, it overloads various operators and functions to provide an entirely native R experience for distributed computations. We showcase how simple it is to port existing R programs to Relem and demonstrate that Relem indeed allows to scale beyond the single-node limitation of R with the full performance of Elemental without any overhead. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation | Linear algebra,Statistical software,Theoretical computer science,Operator (computer programming),Mathematics,Computation,Statistical analysis |
DocType | Volume | Citations |
Journal | abs/1610.07310 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodrigo Canales | 1 | 4 | 1.80 |
| Elmar Peise | 2 | 37 | 5.04 |
| Paolo Bientinesi | 3 | 448 | 53.91 |