Network contention between concurrently running jobs on HPC systems is a primary cause of performance variability. Optimizing job allocation and avoiding network sharing are hence crucial to alleviate the potential performance degradation. In order to do so effectively, an understanding of the interference among concurrently running jobs, their communication patterns, and contention in the network is required. In this work, we choose three representative HPC applications from the DOE Design Forward Project and conduct detailed simulations on a torus network model to analyze both intra-and interjob interference. By scrutinizing the communication behaviors of these applications, we identify relationships between these behaviors and the possible interference introduced by different job placement policies. Our analyses illuminate a path toward communication pattern awareness in job placement on HPC systems.