The static data-flow model of computation promises high-performance from fine-grained parallelism, but conventional token-driven static data-flow architectures are inefficient in terms of memory bandwidth and microcycles required per operation. The associative template mechanism, an application of associative techniques, uses specially configured content-addressable memories to provide efficient flow control for static data-flow program execution. It supports static data-flow semantics while exhibiting memory bandwidth and microcycle demands comparable to those of conventional uniprocessors. Associative diffusion, a second application of associative methods, provides a communication between adjacent nodes of a mesh-connected network of associative-template-based processors. This mechanism achieves nearest-neighbor communication at speeds comparable to intranode transactions by overlapping domains of associativity across boundaries between neighboring nodes. Together, associative templates and associative diffusion provide a token-free approach to static data-flow computation.>