Tuesday 28
Parallel metaheuristics
D. Giménez
› 15:30 - 15:50 (20min)
GPU based metaheuristic for fast controller of an evolutionary robot
Nour El-Houda Benalia  1, *@  , Nesrine Ouannes  2@  , Noureddine Djedi  2, *@  , Yves Duthen  3@  
1 : Laboratoire de Recherche d Systèmes Experts, Imagerie et leurs Applications dans l'ingénierie  (LESIA)  -  Website
2 : Laboratoire de Recherche des Systèmes Experts, Imagerie et leurs Applications dans l'ingénierie  (LESIA)  -  Website
3 : Visual Objects from Reality To Expression  (VORTEX, IRIT)  -  Website
IRIT laboratory, Toulouse
* : Corresponding author

Simulation of sophisticated biological models requires considerable computational power. Modern GPUs enable widely affordable personal computers to carry out massively parallel computation tasks. NVIDIA's CUDA technology provides a wieldy parallel computing platform. Many state-of-the-art algorithms arising from different fields have been redesigned based on CUDA to achieve computational speedup .These new devices opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. This work demonstrates that GPU algorithms represent a significant technological advance for the simulation of complex biological model. Our model is an hybridation of an evolutionary algorithm (EA) and recurrent neural network (RNN) that is developed and tested to generate optimal trajectories of a humanoid robot using GPU Accelerator at multiple levels, taking into consideration an effective utilization of GPU memory hierarchy, judicious management of parallelism, and some techniques of optimizations of GPU codes. The model was derived from our CPU serial implementation. This paper presents the implementation details of our proposal on PC which has 2 GTX 480 GPUs. This combination constructs a controller for the simulated humanoid robot which is simulated using the library named Open Dynamic Engine (ODE). Since EA and RNN are inherently parallel, the GPGPU computing paradigm leads to a promising speedup in the evolutionary phase with respect to the CPU based version. 

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