Abstract
An experimental approach is chosen to investigate the performance of a fine-grained dataflow architecture for numerically intensive digital signal processing (DSP) applications. The focus is on the behavior of pipelined data-parallel algorithms. However, the granularity of the high-level language programming blocks is not explicitly optimized to balance computation and communication; a natural and logical fine-grained decomposition of problems is used instead. The authors interpret their empirical data by means of parameters such as a number of instructions per generic unit of computation, a density of precedence relations, and a serial fraction. The performance and limitations of fine-grained general-purpose dataflow computing are discussed. >
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