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 of the requirements of current and emerging paradigms of scientific computing in terms of their computation and communication demands.

The SRC reconfigurable computer provides the capability of obtaining application-specific driven performance for high data bandwidth, computationally intensive applications. It has high-density FPGA devices with local distributed memory banks that can be utilized to obtain high performance for floating point applications. The floatingpoint Sparse Matrix Vector (SpMatVec) multiplication, a key computation kernel in many scientific applications does not run at peak performance on general purpose microprocessors. The high I/O bandwidth and avoidance of cache-hierarchy architecture in this reconfigurable computer allow us to efficiently implement the floatingpoint SpMatVec kernel on the SRC platform. In this paper we investigate the implementation of a floating-point SpMatVec kernel on the SRC MAPstation and benchmark its performance against other general-purpose microprocessor-based implementations.

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 of the requirements of current and emerging paradigms of scientific computing in terms of their computation and communication demands.