In scheduling, from both theoretical and practical points of view, a set of machines in Parallel is a setting that is important. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view, the occurrence of resources in Parallel is common in real-world. When machines are computers, a Parallel program is necessary because the members of the program are performed in a Parallel fashion, and this performance is executed according to some precedence relationship. This paper shows the problem of allocating a number of non-identical tasks in a multi-processor or multicomputer system. The model assumes that the system consists of a number of identical processors, and only one task may be executed on a processor at a time. Moreover, all schedules and tasks are non-preemptive.

algorithmic skeleton has received attention as an efficient method of Parallel programming in recent years. Using the method, the programmer can implement Parallel programs easily. In this study, a set of efficient algorithmic skeletons is introduced for use in implementing Parallel genetic algorithm (PGA).A performance model is derived for each skeleton that makes the comparison of skeletons possible in order to select the best one for the application. The performance of the selected skeleton can be increased by specifying the virtual topology required by the application. This is a novel approach with no precedent Nesting of skeletons used here is another novelty of the study which has been employed only in few previous studies.

Bacterial foraging algorithm is one of the population-based optimization algorithms that used for solving many search problems in various branches of sciences. One of the issues discussed today is Parallel implementation of population-based optimization algorithms on Graphic Processor Units. Due to the low speed of bacterial foraging algorithm in the face of complex problem and also lack the ability to solve large-scale problems by this algorithm, Implementation on the graphics processor is a suitable solution to cover the weaknesses of this algorithm. In this paper, we proposed a Parallel version of bacterial foraging algorithm which designed by CUDA and has ability to run on GPUs. The performance of this algorithm is evaluated by using a number of famous optimization problems in comparison with the standard bacterial foraging optimization algorithm. The results show that Parallel algorithm is faster and more efficient than standard bacterial foraging optimization algorithm.

Now diagnostic methods with the help of machine learning have been able to help doctors in this field. One of the most important of these methods is deep learning, which has gotten good answers in images containing cancer. Increasing the accuracy of deep neural network classifiers can increase the diagnosis of breast cancer. In this paper, we have tried to achieve higher accuracy than non-Parallel models with the help of a Parallel model of a deep neural network. The proposed method is a Parallel hybrid method combining AlexNet and VGGNet networks applied in Parallel to mammographic images. The database used in this article is INBreast. The results obtained from this method show a 4% increase compared to some other classification models so that in the type of density 1, it has achieved about 99.7%. In the case of other densities, an accuracy of nearly 99% has been obtained.

In this paper, we study different methods of solving joint redundancy-availability optimization for series-Parallel systems with multi-state components. We analyzed various effective factors on system availability in order to determine the optimum number and version of components in each sub-system and consider the effects of improving failure rates of each component in each sub-system and improving reliability of each sub-system. The target is to determine optimum values of all variables for improving the availability level and decreasing the total cost of the system. At first, the exact values of variables are determined using a mathematical model, then, the results of SAParallel, VDO-Parallel and genetic algorithms are compared with the exact solution.

A mixed dominating set S of a graph G = (V; E) is a subset of vertices and edges like S  V [ E such that each element v 2 (V [ E) n S is adjacent or incident to at least one element in S. The mixed domination number m(G) of a graph G is the minimum cardinality among all mixed dominating sets in G. The problem of finding m(G) is known to be NP-complete. In this paper, we present an explicit polynomial-time algorithm using the parse tree to construct a mixed dominating set of size m(G) where G is a generalized series-Parallel graph.

The map-reduce model is a method for executing large data applications. It is also a Parallel programming model for writing applications that can be executed on the cloud. Organizations are increasingly producing data that is generated by business processes, user activities, website tracking, sensors, finance, accounting, and more. Data clustering algorithms are used as tools for analyzing large volumes of data. The main purpose of these algorithms is to categorize data into clusters so that the data objects in each cluster are more similar. In this paper, a dense hierarchical clustering algorithm, one of the data mining techniques, is implemented using map-reduce design and then the results of this algorithm are compared with the usual one. Experiments show that runtime decreases with increasing input data size. The runtime of the algorithm improved by 16. 80% for the 200 data-point dataset and 29. 26% for the dataset with 1000 data points. The percentage of CPU usage in the Parallel system also increased from 22% to 94%.