Optimal design-space exploration of streaming applications

Many embedded and scientific applications are frequently pipelined asynchronously and deployed on architecturally diverse systems to meet performance requirements and resource constraints. We call such pipelined applications streaming applications. Typically, there are several design parameters in the algorithms and architectures used that, when customized, impact the tradeoff between different metrics of application performance as well as resource utilization. Automatic exploration of this design space is the goal of this research. When using architecturally diverse systems to accelerate streaming applications, the design search space is often complex. We present a global optimization framework comprising a novel domain-specific variation of branch-and-bound that reduces search complexity by exploiting the topology of the application's pipelining. We exploit the topological information to discover decomposability through the canonical Jordan block form and to originate two novel decomposition techniques that we name linear chaining and convex decomposition. The reduction in search complexity that we achieve for two prototypical real-world streaming applications is significant. For one application the size of the search space reduces from about 10^18 to 10^12