ElsevierGarcía Mollá, VM.; Liberos Mascarell, A.; Vidal Maciá, AM.; Guillem Sánchez, MS.; Millet Roig, J.; González Salvador, A.; Gonzalez, A.... (2014). Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units. Computers in Biology and Medicine. 44:15-26. doi:10.1016/j.compbiomed.2013.10.023. Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units
AbstractIn this paper we studied the implementation and performance of adaptive step methods for large systems of ordinary differential equations systems in Graphics Processing Units, focusing on the simulation of thre-dimensional electric cardiac activity. The RushLarsen method was applied in all of the implemented solvers to improve efficiency. We compared the adaptive methods with the fixed step methods, and we found that the fixed step methods can be faster while the adaptive step methods are better in terms of accuracy and robustness.
ElsevierGarcía Mollá, VM.; Simarro Haro, MDLA.; Martínez Zaldívar, FJ.; González Salvador, A.; Vidal Maciá, AM. (2016). Maximum likelihood soft-output detection through Sphere Decoding combined with box optimization. Signal Processing. 125:249-260. Abstract This paper focuses on the improvement of known algorithms for maximum likelihood soft-output detection. These algorithms usually have large computational complexity, that can be reduced by using clipping. Taking two well-known soft-output maximum likelihood algorithms (Repeated Tree Search and Single Tree Search) as a starting point, a number of modifications (based mainly on box optimization techniques) are proposed to improve the efficiency of the search. As a result, two new algorithms are proposed for soft-output maximum likelihood detection. One of them is based on Repeated Tree Search (which can be applied with and without clipping). The other one is based on Single Tree Search, which can only be applied to the case with clipping. The proposed algorithms are compared with the Single Tree Search algorithm, and their efficiency is evaluated in standard detection problems (4x4 16-QAM and 4x4 64-QAM) with and without clipping. The results show that the efficiency of the proposed algorithms is similar to that of the Single Tree Search algorithm in the case 4x4 16-QAM; however, in the case 4x4 64-QAM, the new algorithms are far more
Border tracking in binary images is an important kernel for many applications. There are very efficient sequential algorithms, most notably, the algorithm proposed by Suzuki et al., which has been implemented for CPUs in well-known libraries. However, under some circumstances, it would be advantageous to perform the border tracking in GPUs as efficiently as possible. In this paper, we propose a parallel version of the Suzuki algorithm that is designed to be executed in GPUs and implemented in CUDA. The proposed algorithm is based on splitting the image into small rectangles. Then, a thread is launched for each rectangle, which tracks the borders in its associated rectangle. The final step is to perform the connection of the borders belonging to several rectangles. The parallel algorithm has been compared with a state-of-the-art sequential CPU version, using two different CPUs and two different GPUs for the evaluation. The computing times obtained show that in these experiments with the GPUs and CPUs that we had available, the proposed parallel algorithm running in the fastest GPU is more than 10 times faster than the sequential CPU routine running in the fastest CPU.
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