SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Developer - Algorithms
Presentation
Media
Parallel Ant Colony Optimization with CUDA
Speakers:
Octavian Nitica
- University of Delaware
Abstract:
The Ant Colony Optimization (ACO) Algorithm is a metaheuristic that is used to find shortest paths in graphs. By using CUDA to implement an ACO algorithm, we achieved significant improvement in performance over a highly-tuned sequential CPU implementation. The construction step of the ACO algorithm consists of each ant creating an independent solution, and this step is where most of the computation is spent. Since the construction step is the same for most ACO variations, parallelizing this step will also allow for easy adaptation to different pheromone updating functions. Currently, our research tests this hypothesis on the travelling salesmen problem.
 
Topics:
Developer - Algorithms
Type:
Poster
Event:
GTC Silicon Valley
Year:
2010
Session ID:
P10A04
Download:
Share: