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This paper examines the explicit communication characteristics of several sophisticated scientific applications, which, by themselves, constitute a representative suite of publicly available benchmarks for large cluster architectures. By focusing on the Message Passing Interface (MPI) and by using hardware counters on the microprocessor, we observe each application's inherent behavioral characteristics: point-to-point and collective communication, and floating point operations. Furthermore, we explore the sensitivities of these characteristics to both problem size and number of processors. Our analysis reveals several striking similarities across our diverse set of applications including the use of collective operations, especially those collectives with very small data payloads. We also highlight a trend of novel applications parting with regimented, static communication patterns in favor of dynamically evolving patterns, as evidenced by our experiments on applications that use implicit linear solvers and adaptive mesh refinement. Overall, our study contributes a better understanding or the requirements of current and emerging paradigms of scientific computing in terms of their computation and communication demands.