Name
Abstract | ||
|---|---|---|
We summarize the need and present our vision for accelerating geo-spatial computations and analytics using a combination of shared and distributed memory parallel platforms, with general-purpose Graphics Processing Units (GPUs) with 100s to 1000s of processing cores in a single chip forming a key architecture to parallelize over. A GPU can yield one-to-two orders of magnitude speedups and will become increasingly more affordable and energy efficient due to mass marketing for gaming. We also survey the current landscape of representative geo-spatial problems and their parallel, GPU-based solutions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2766196.2766200 | SIGSPATIAL Special |
DocType | Volume | Issue |
Journal | 6 | 3 |
Citations | PageRank | References |
5 | 0.41 | 23 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prasad, Sushil K. | 1 | 355 | 46.44 |
| Michael McDermott | 2 | 11 | 1.86 |
| Satish Puri | 3 | 46 | 8.79 |
| Dhara Shah | 4 | 7 | 0.79 |
| Danial Aghajarian | 5 | 8 | 1.47 |
| Shashi Shekhar | 6 | 4352 | 1098.43 |
| Xun Zhou | 7 | 877 | 56.21 |