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      • Open Access Article

        1 - Investigating the Effects of Hardware Parameters on Power Consumptions in SPMV Algorithms on Graphics Processing Units (GPUs)
        Farshad Khunjush
        Although Sparse matrix-vector multiplication (SPMVs) algorithms are simple, they include important parts of Linear Algebra algorithms in Mathematics and Physics areas. As these algorithms can be run in parallel, Graphics Processing Units (GPUs) has been considered as on More
        Although Sparse matrix-vector multiplication (SPMVs) algorithms are simple, they include important parts of Linear Algebra algorithms in Mathematics and Physics areas. As these algorithms can be run in parallel, Graphics Processing Units (GPUs) has been considered as one of the best candidates to run these algorithms. In the recent years, power consumption has been considered as one of the metrics that should be taken into consideration in addition to performance.  In spite of this importance, to the best of our knowledge, studies on power consumptions in SPMVs algorithms on GPUs are scarce.  In this paper, we investigate the effects of hardware parameters on power consumptions in SPMV algorithms on GPUs. For this, we leverage the possibility of setting the GPU’s parameters to investigate the effects of these parameters on power consumptions. These configurations have been applied to different formats of Sparse Matrices, and the best parameters are selected for having the best performance per power metric. Therefore, as the results of this study the settings can be applied in running different Linear Algebra algorithms on GPUs to obtain the best performance per power. Manuscript profile
      • Open Access Article

        2 - Investigating the Effect of Hardware Parameters Adjustments on Energy Consumption in Thin Matrix Multiplication Algorithm on GPUs
        mina ashouri Farshad Khunjush
        Multiplication of thin algorithmic matrices is a simple but very important part of linear and scientific algebra programs in mathematics and physics, and due to its parallel nature, GPUs are one of the most suitable and important options. To select its executive platfor More
        Multiplication of thin algorithmic matrices is a simple but very important part of linear and scientific algebra programs in mathematics and physics, and due to its parallel nature, GPUs are one of the most suitable and important options. To select its executive platform. In recent years, due to the emphasis of researchers to consider energy consumption as one of the main design goals along with efficiency, very little effort has been made to improve the energy consumption of this algorithm on the GPU. In this article, this issue is addressed from the perspective of energy efficiency in efficiency obtained. Utilizing the configuration capability introduced in modern GPUs, by statistically examining the behavior of this algorithm when using different thin matrix storage formats and different hardware settings for more than 200 matrices Slim example, the best configuration settings for the thin matrix multiplication algorithm with different storage formats on the GPU are obtained. This configuration for each storage format is selected to give the best configuration in all samples tested. Manuscript profile
      • Open Access Article

        3 - Using a multi-objective optimization algorithm for tasks allocate in the cloud-based systems to reduce energy consumption
        sara tabaghchimilan nima jafari novimipour
        Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amount More
        Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amounts of electrical power. On the other hand, inadequate and inadequate cooling systems not only cause excessive heating of resources and shorten the life of the machines. It also produces carbon that plays an important role in the weather. Therefore, they should reduce the total energy consumption of these systems with proper methods. In this research, an efficient energy management approach is provided in virtual cloud data centers, which reduces energy consumption and operational costs, and brings about an increase in the quality of services. It aims to provide a resource allocation strategy for cloud systems with the goal of reducing energy, cost of implementation and examining its use in cloud computing. The results of the simulation show that the proposed method in comaprision to NPA, DVFS, ST and MM methods can reduce the average energy consumption up to 0.626 kWh, also the need to immigration and SLA violation declined up to 186 and 30.91% respectively. Manuscript profile