MASSIVELY PARALLEL IMPLEMENTATION OF GAUSSIAN SMOOTHING ON MANY-CORE ARCHITECTURES
Keywords:
Parallel Computing, Image Convolution, Gaussian Blur, Google Colaboratory.Abstract
As image processing techniques demand higher performance, traditional single-threaded convolution methods become increasingly inadequate. This study focuses on the parallel implementation of 2D Gaussian convolution to overcome these computational hurdles. By offloading the workload to parallel systems, we significantly reduce processing latency. The paper evaluates the efficiency of Gaussian filtering on heterogeneous platforms, specifically contrasting multi-core CPU architectures against GPU acceleration. Experimental results, conducted on the Google Colab platform, demonstrate the superior speedup achieved through hardware-level parallelization.
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