Coronal mass ejection (CME) is considered to be the main driving force of space weather disasters. CME often appears with many other solar activities. Since coronal dimming is associated most closely with CME, the detection of coronal dimming can help to forecast CME. Continuous development of CME observations brings increasingly large amount of data, whereas the efficiency of the detection algorithms of coronal dimming needs to be improved. Three parallel algorithms are presented for a semi-automatic dimming detection algorithm. They make the foundation for real-time detection of coronal dimming. We firstly introduce the existing work about the topic, then analyze a semi-automatic dimming detection algorithm, which to some degree, improves the efficiency of artificial recognition of darkened area, but does not meet the requirements of real-time detection. Based on the principle of parallelism of Matlab R2014a, three different parallel algorithms are presented from different aspects, i.e. data, distance-calculation and image-divide. Experimental results show that the parallelization based on image-divide is the most efficient one among the three algorithms.