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For the past 40 years, Moore’s law has predicted the rapid growth of the computer industry. In the past few years, however, this growth has slowed for central processing units (CPUs). Instead, there has been a shift to multicore computing, specifically with the general purpose graphic processing units (GPUs). Conventional CPUs have between two and eight cores, but the GPUs can have hundreds, even...
With the increased connectivity of computer devices including mobile devices to the Internet, the volume as well as the type of information that moved through organization network perimeter are also growing significantly. Consequently, this has heightened information security threats to organizations that rely on the Internet to do their businesses. On the other hand, Intrusion Detection System (IDS)...
The use of GPU clusters for scientific applications in areas such as physics, chemistry and bioinformatics is becoming more widespread. These clusters frequently have different types of processing devices, such as CPUs and GPUs, which can themselves be heterogeneous. To use these devices in an efficient manner, it is crucial to find the right amount of work for each processor that balances the computational...
The programming model of general propose computing on graphic processing units (GPGPU) offers great efficiency for applications acceleration. This feature is granted by the ability of partitioning a sequential application into smaller subproblems with high computing requirements; those subproblems can be executed in parallel by a graphics processing unit (GPU) and partial results can be transferred...
In this paper, several methods of optimizing parallel implementation of 2D FDTD algorithm are presented. Some practical problems occurring in real simulations are taken into consideration. Moreover, the presented methods are supported with appropriate tests and practical examples.
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