Many big data applications are usually categorized as irregular. Irregular problems feature unpredictable and unstructured properties in terms of the program flow, data access pattern and typically use pointer-based data structures such as graphs. The problems are data, compute and communication intensive in nature. The algorithms are therefore designed and implemented on high performance architectures. The first stage of the parallel algorithm design is data partitioning. In this stage, the data is sub-divided into equally sized disjoint elements such that the communication volume among the processors is minimized. If the data is represented as a graph, it can be stated as the graph partitioning problem, which is NP-hard. In this work, we consider the metaheuristic, ant brooding algorithm based on larval sorting by ants to solve the graph partitioning problem. The parallel ant brooding algorithm is implemented on a cluster using MIT's Julia language. We test the parallel algorithm on different benchmark and synthetic graphs. We compare our Julia parallel implementation with Julia sequential and C sequential implementations. We found that the performance of Julia is comparable to C with good scalability, and the parallel Julia implementation achieves speedup greater than 1 for a synthetic graph with 200 vertices and 1000 edges.