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G-PAS 2.0 - an improved version of protein alignment tool with an efficient backtracking routine on multiple GPUs

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Frohmberg W. , Kierzynka M. , Blazewicz J. , Wojciechowski P.

tom Vol. 60, nr 3

491-494

Several highly efficient alignment tools have been released over the past few years, including those taking advantage of GPUs (Graphics Processing Units). G-PAS (GPU-based Pairwise Alignment Software) was one of them, however, with a couple of interesting features that made it unique. Nevertheless, in order to adapt it to a new computational architecture some changes had to be introduced. In this paper we present G-PAS 2.0 - a new version of the software for performing high-throughput alignment. Results show, that the new version is faster nearly by a fourth on the same hardware, reaching over 20 GCUPS (Giga Cell Updates Per Second).

Cuda Based Fuzzy C-Means Acceleration for the Segmentation of Images with Fungus Grown in Foam Matrices

100%

Rowińska Z. , Gocławski J.

Image Processing & Communications

2012

tom Vol. 17, no. 4

191--200

In the paper authors verify the advantages of GPU computing applied to fuzzy c-means segmentation. Three different algorithms implementing FCM method have been compared by their execution times. All tests refer to the images of polyurethane foam matrices filled with fungus (mould). They are aimed at separating mould regions from the matrix base. The authors proposed a method using CUDA programming tools, which significantly speedsup FCM computations with multiple cores built in a graphic card.

Simulating P Systems on GPU Device : A Survey

88%

Martínez-del-Amor M. A. , García-Quismondo M. , Macías-Ramos L. F. , Valencia-Cabrera L. , Riscos-Núñez A. , Pérez-Jiménez M. J.

Fundamenta Informaticae

2015

tom Vol. 136, nr 3

269--284

P systems have been proven to be useful as modeling tools in many fields, such as Systems Biology and Ecological Modeling. For such applications, the acceleration of P system simulation is often desired, given the computational needs derived from these kinds of models. One promising solution is to implement the inherent parallelism of P systems on platforms with parallel architectures. In this respect, GPU computing proved to be an alternative to more classic approaches in Parallel Computing. It provides a low cost, and a manycore platform with a high level of parallelism. The GPU has been already employed to speedup the simulation of P systems. In this paper, we look over the available parallel P systems simulators on the GPU, with special emphasis on those included in the PMCGPU project, and analyze some useful guidelines for future implementations and developments.