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The emergence of chiplet-based architectures represents a paradigm shift in post-Moore’s Law computing systems, offering substantial cost and yield advantages through functional disaggregation. However, the heterogeneity of inter-chiplet communication introduces unique performance challenges that conventional partitioning strategies fail to address. This work presents a comprehensive characterization of how poor workload partitioning degrades communication performance in chiplet-based systems. We demonstrate, through detailed experimental analysis, that suboptimal workload partitioning can increase inter-chiplet communication latency by up to 10×, and can inflate network congestion beyond sustainable levels as systems scale. Our findings show that optimized partitioning strategies can achieve 87.4% reduction in inter-chiplet traffic, improve system throughput by 8.75×, and enhance energy efficiency by 10.3× compared to naive partitioning approaches. We further characterize how these effects compound with system scalability, revealing that communication overhead can consume 85% of execution time in poorly partitioned 16-chiplet systems, versus only 35% in well partitioned configurations. This work provides essential insights into the communication-aware design space of chiplet systems and validates the critical importance of sophisticated workload partitioning algorithms.