We present a novel 2-approximation algorithm for deploying stream graphs on multicore computers and a stream graph transformation that eliminates bottlenecks. The key technical insight is a data rate transfer model that enables the computation of a "closed form", i.e., the data rate transfer function of an actor depending on the arrival rate of the stream program. A combinatorial optimization problem uses the closed form to maximize the throughput of the stream program. Although the problem is inherently NP-hard, we present an efficient and effective 2-approximation algorithm that provides a lower bound on the quality of the solution. We introduce a transformation that uses the closed form to identify and eliminate bottlenecks. We show experimentally that state-of-the art integer linear programming approaches for orchestrating stream graphs are (1) intractable or at least impractical for larger stream graphs and larger number of processors and (2) our 2-approximation algorithm is highly efficient and its results are close to the optimal solution for a standard set of StreamIt benchmark programs.