Performance Tuning of Streaming Applications via Search-space Decomposition

High-performance streaming applications are typically pipelined and deployed on architecturally diverse (hybrid)systems. Developers of such applications are interested in customizing components used, so as to benefit application performance. We present an efficient and automatic technique for design-space exploration of applications in this problem domain. We solve performance tuning as an optimization problem by formulating cost functions using results from queueing theory. This results in a mixed-integer nonlinear optimization problem which is NP-hard. We reduce the search complexity by decomposing the search space. We have developed a domain-specific decomposition technique using topological information of the application embodied in the queueing network models. Our analysis includes when our decomposition preserves optimality. Our preliminary empirical results confirm two-fold benefits--solving a problem that is currently not solvable using state-of-the-art solvers and in some problem instances, improving initial solution value from the solver by over two orders of magnitude