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With the increasing consumption of energy in data centers, the demand for energy efficient multiprocessor job scheduling is growing dramatically. Besides flow time, energy conservation has become a significant issue and drawn enormous interest. In this paper, an online non-clairvoyant scheduling algorithm Significance-based Multiprocessor Round Robin (SbMRR) is proposed. SbMRR utilizes the unbounded speed model, where the range of the speed of any processor is from zero to infinity. To validate the effectiveness of the algorithm, mathematical and simulation-based analysis are conducted which demonstrates that SbMRR provides the minimum sum of significance-based flow time and energy consumed. SbMRR is O(α)-competitive, more precisely ((α + 1)⁄((1–1⁄αβ)))-competitive, where β ≥ 4 a constant. The competitive ratio of SbMRR is least to date. SbMRR provides the minimum sum of energy consumed and significance-based flow-time. [ABSTRACT FROM AUTHOR]

Aerial image simulation is a fundamental problem in advanced lithography for chip fabrication. Since it requires a huge number of mathematical computations, an efficient yet accurate implementation becomes a necessity. In the literature, graphic processing unit (GPU) or multi-core single instruction multiple data (SIMD) CPU has demonstrated its potential for accelerating simulation. However, the combination of GPU and multi-core SIMD CPU was not exploited thoroughly. In this paper, we present and discuss collaborative computing algorithms for the aerial image simulation on multi-core SIMD CPU and GPU. Our improved method achieves up to 160× speedup over the baseline serial approach and outperforms the state-of-the-art GPU-based approach by up to 4× speedup with a hex-core SIMD CPU and Tesla K10 GPU. We show that the performance on the collaborative computing is promising, and the medium-grained task scheduling is suitable for improving the collaborative efficiency. [ABSTRACT FROM AUTHOR]

An improved image magnification algorithm for gray and color images is presented in this paper to meet the challenge of preserving high-frequency components of an image, including both image edges and texture structures. In the proposed algorithm, a new edge detection method that uses the well-known Otsu automatic optimum thresholding is proposed to distinguish strong edge pixels. The parameters of the original directional cubic convolution interpolation algorithm, which were selected based on training, were eliminated. As a result, our algorithm achieves more accurate edge detection, better interpolation results, and less computational complexity. Simulation results demonstrate that the improved algorithm can reconstruct the magnified image, preserve edges and textures simultaneously, and reduce common interpolation artifacts. Furthermore, it generates higher visual quality of the magnified images and achieves higher peak signal-to-noise ratio, structural similarity, and feature similarity compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]