Presentation of the GPU architecture and of the CUDA environment

2013 ◽  
pp. 25-34
Keyword(s):  
Gpu Architecture

DeF-GPU: Efficient and effective deletions finding in hepatitis B viral genomic DNA using a GPU architecture

Methods ◽  
2016 ◽  
Vol 111 ◽  
pp. 56-63 ◽  
Author(s):  
Chun-Pei Cheng ◽  
Kuo-Lun Lan ◽  
Wen-Chun Liu ◽  
Ting-Tsung Chang ◽  
Vincent S. Tseng
Keyword(s):  
Hepatitis B ◽  
Genomic Dna ◽  
Gpu Architecture

Gpu architecture for stationary multisensor pedestrian detection at smart intersections

2011 ◽  
Author(s):  
Daniel Weimer ◽  
Sebastian Kohler ◽  
Christian Hellert ◽  
Konrad Doll ◽  
Ulrich Brunsmann ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Gpu Architecture

GPU Architecture and Programming Challenges

2020 ◽  
pp. 123-136
Author(s):  
Unnikrishnan Cheramangalath ◽  
Rupesh Nasre ◽  
Y. N. Srikant
Keyword(s):  
Gpu Architecture

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2020 ◽  
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Departure Point ◽  
Classic Problem ◽  
Iot Devices ◽  
Vehicle Travel Time ◽  
Gpu Architecture

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

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