Lattice star and acyclic branched polymer vertex exponents in 3d

Numerical values of lattice star entropic exponents $\gamma_f$, and star vertex exponents $\sigma_f$, are estimated using parallel implementations of the PERM and Wang-Landau algorithms. Our results show that the numerical estimates of the vertex exponents deviate from predictions of the $\eps$-expansion and confirms and improves on estimates in the literature. We also estimate the entropic exponents $\gamma_\mathcal{G}$ of a few acyclic branched lattice networks with comb and brush connectivities. In particular, we confirm within numerical accuracy the scaling relation $$ \gamma_{\mathcal{G}}-1 = \sum_{f\geq 1} m_f \, \sigma_f $$ for a comb and two brushes (where $m_f$ is the number of nodes of degree $f$ in the network) using our independent estimates of $\sigma_f$.

Off-lattice and parallel implementations of the pivot algorithm

The pivot algorithm is the most efficient known method for sampling polymer configurations for self-avoiding walks and related models. Here we introduce two recent improvements to an efficient binary tree implementation of the pivot algorithm: an extension to an off-lattice model, and a parallel implementation.

APRIMORAMENTOS DA SOLUÇÃO PARALELA BASEADA EM OPERAÇÕES COLETIVAS PARA O BOOTSTRAP DA RECONSTRUÇÃO DE ÁRVORES FILOGENÉTICAS NO PHYML 3.0

Phylogenetics determines the evolutionary relationships between groups of species, through a phylogenetic tree. PhyML is among the main programs for the reconstruction of phylogenetic trees. Bootstrap is a statistical method used to measure the confidence of a given data set, which is usually applied in the analysis of inferred phylogenetic trees. In PhyML this method has two MPI parallel implementations: with point-to-point operations and collective operations. The second version is more efficient than the first, however it has a limitation on the number of bootstrap to be used due to the increase in memory consumption. In order to solve this problem, three proposals were developed. The objectives of this work were to carry out the validation of these versions together with performance tests. The validation showed that the proposed solutions present results equivalent to the point-to-point version. In the performance simulations, two solutions were shown to be superior to the point-to-point version, with the best one achieving gains of 28.46% and 39.64% for 32 and 64 processes, respectively. Therefore, the enhancements allow alternatives to the point-to-point version without limitingmemory.

PARALLELIZATION OF RSA CRYPTOGRAPHIC ALGORITHM BASED ON CUDA TECHNOLOGIES

The paper examines the efficiency of the application of CUDA technologies for the parallelization of the cryptographic algorithm with the public key. The speed of execution of several implementations of the algorithm is compared: sequential implementation on the CPU and two parallel implementations – on the CPU and GPU. A description of the public key algorithm is presented, as well as properties that allow it to be parallelized. The advantages and disadvantages of parallel implementations are analyzed. It is shown that each of them can be suitable for different scenarios. The software was developed and several numerical experiments were performed. The reliability of the obtained results of encryption and decryption is confirmed. To eliminate the influence of external factors at the time of execution the algorithm was tested ten times in a row and the average value was calculated. Acceleration coefficients for message encryption and decryption algorithms were estimated based on OpenMP and CUDA technology. The proposed approach focuses on the possibility of further optimization through the prospects of developing a multi-core architecture of computer systems and graphic processors.