Blocking is a technique of filtering unlikely matched pairs for record matching, which aims to collect all pairs of records that relate to the same entities across different data sources. Blocking has been broadly adopted in data mining and database. However, for big data, there is no fast and effective blocking algorithm yet, because the number of candidate pairs is tremendous between large data sets. In this paper, we report on a probabilistic parallelisation of a recently proposed blocking that is a sequential algorithm for efficient record matching in single machines. Our approach runs blocking processes distributedly on partitioned input data. In order to reduce data exchange among those blocking processes, we adopt a probabilistic technique to assure that the processes can run independently and meanwhile the aggregated result is correct with respect to common metrics. Our experimental analysis endorses the advantage of our technique and shows its novel scalability on a Hadoop map-reduce system deployed physically in a cloud.