Name
Abstract | ||
|---|---|---|
Graph ranking algorithms, such as PageRank, are widely used in a number of real-world applications like web search. As the size of the graphs on which these algorithms are applied gets bigger and bigger, it becomes necessary to devise powerful and flexible techniques to accelerate and parallelize the computation, both at software and hardware level. Leveraging GPUs is a promising direction due to their highly parallel computing capabilities, but execution time is often hampered by write conflicts. In this paper, we present a solution to handle write conflicts in GPU computations exploiting high level of parallelism, and show how this technique can effectively be used to accelerate the computation of PageRank by a factor of 5x, with respect to a baseline in which conflicts are not handled. Our solution is implemented at software level, and doesn't require specific hardware resources. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/RTSI.2019.8895572 | 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI) |
Keywords | Field | DocType |
Graph Algorithms,GPU,Atomic Addition,PageRank | PageRank,Learning to rank,Graph,Graph algorithms,Computer science,Parallel computing,Software,Execution time,Computation | Conference |
ISSN | ISBN | Citations |
2687-6809 | 978-1-7281-3816-9 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego Piccinotti | 1 | 0 | 0.34 |
| Edoardo Ramalli | 2 | 0 | 0.34 |
| Alberto Parravicini | 3 | 1 | 2.05 |
| Rolando Brondolin | 4 | 5 | 3.55 |
| Marco D. Santambrogio | 5 | 771 | 91.15 |