Distributed Frequent Pattern Mining (DFPM) is a well known technique of Distributed Data Mining (DDM) that deals with finding interesting frequent patterns from a huge dataset in a distributed environment. Many algorithms do exist for mining such huge dataset in distributed environment but still it is an interesting problem because of the exponential growth of variety of data from various sources. In this paper, we have proposed a novel distributed frequent pattern mining algorithm using load-matrix. This algorithm split the dataset vertically into multiple loads which are assigned to the available cores in the system for parallel execution. From the experimental results it is observed that the proposed algorithm outperforms the existing apriori algorithm.