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Figure 6 illustrates our proposed working model of the radiation-induced bystander signalling pathway involved.
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In "Proposed work" section, we give the points of interest of our proposed work and algorithm of HCudaBLAST, and in "Experimental setup" section, we give description of our experimental setup, followed by conclusion in "Conclusion" section.
In our proposed work we discuss a design of resource allocation protocol and its performance evaluation.
We present all the relevant steps in our proposed work-flow and evaluate the results.
Our proposed work asks: what are the implications for analysis and inference when using high-resolution data with different error structures?
Our proposed work deals with real time task which is not static, and concentrates on the dynamic users since they are involved in cloud.
Comprehensive evaluation on standard benchmark datasets and comparison with nine state-of-the-art methods validate the effectiveness of our proposed work.
Our proposed work improves the GPU FDTD results by increasing the global memory coalescence with the same amount of occupancy, resulting in an increase in maximum output performance.
Apart from the said resource dynamics, our proposed work also considers the processor transitions to various sleep states and their corresponding wake up latencies that are inherent in contemporary enterprise servers.
Although both GPU based programs are over 28 times faster than a sequential CPU based version, Implementation of our proposed work showed up to 44% decrease in execution time comparing to the naive GPU method.
Our proposed work takes a significant step toward hybrid energy harvesting from human-body-induced motions such as handshaking, walking, running and its potential applications in self powered portable electronics.
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