![]() ![]() Instead of utilizing a hardware-accelerated approach, we propose a purely software-based method which is simpler and cheaper. ![]() Sobel edge detection is widely used in computer vision and image processing but its processing time becomes a serious problem in real-time environments, especially when an image is very large. Our framework achieved speedups of up to a factor of 3.49x, within 20.5% of the theoretically possible maximum speedup. We conducted an extensive experimental evaluation on a set of eight benchmarks using up to eight nodes (288 vCPUs) in the AWS public cloud. All computations occur in the Cloud, with minimal user intervention. A combination of offline profiling, dynamic simulation, and scheduling are utilized to select optimal tile sizes, schedule, and execute matrix operations. Instead, users employ a novel matrix data type with lazy evaluation semantics to facilitate implicit parallelization of matrix operations. With this framework, users are shielded from the complexity of explicitly parallel computations. We present a new framework called cloud matrix machine, which extends the Julia high-performance compute language to automatically parallelize matrix computations for the cloud. But with current matrix language implementations it is a challenging task to fully utilize Cloud compute capacities. Matrix computations are widely used in increasing sizes and complexity in the fields of scientific computing and engineering. On the other hand, the average insertion loss and noise of optical routers are increased by 2.94 dB and 2.95 dB, respectively. The simulation results show that upon the application of the improved genetic optimization routing algorithm to the improved mesh 3D PNoC topology, the average end-to-end delay is reduced by 32.9%, the throughput rate is increased by 28.5%, and the system power consumption is reduced by 27.6%. Compared with the case of traditional optoelectronic devices, the performance of on-chip optical networks can be improved effectively. On this basis, an improved genetic optimization routing algorithm is implemented on the improved mesh 3D PNoC topology, which completes the data exchange of the IP core of the network on the optical chip. SPP switching and SPP MRR are combined to form a fault-tolerant SPP router. An SPP hybrid silicon-based electro-optic modulator and an improved fault-tolerant SPP router are used to improve the performance of the network on an optical chip. This paper improves the mesh 3-dimensional photonic network-on-chip (3D-PNoC) topology. The performance of electro-optic modulators and optical routers and their routing algorithms are the key factors affecting the performance of networks on optical chips. ![]()
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