Code generation for energy‐efficient execution of dynamic streaming task graphs on parallel and heterogeneous platforms

Abstract

Summary Streaming task graphs are high‐level specifications for parallel applications operating on streams of data. For a static task graph structure, static schedulers can be used to map the tasks onto a parallel platform to minimize energy consumption for given throughput. We introduce dynamic elements into the task graph structure, thus specifying applications which adapt behavior at runtime, for example, switching from check‐only to active mode. This in turn necessitates a runtime system that can remap tasks and potentially adapt their degree of parallelism in case of a dynamic change of the task structure. We provide a toolchain and evaluate our prototype with streaming task graphs both synthetic and from a real application. We find that we meet throughput requirements with <3.5% energy overhead on average compared with an optimal static scheduler based on integer linear programming. Runtime overhead for remapping is negligible and application runtime and energy are accurately predicted. We also outline how to extend our system to a heterogeneous platform.

Integrating Energy-Optimizing Scheduling of Moldable Streaming Tasks with Design Space Exploration for Multiple Core Types on Configurable Platforms

“…In addition to the synthetic task sets considered in [5], we were interested in whether combined design space exploration and scheduling could offer any benefits when it comes to real applications. Therefore, we have performed the above experiments for three selected applications: H.263 encode detailed in [11], stereo depth estimation (SDE) from [12], and edge detection as described in [13]. The H.263 encode task set is comprised of 9 sequential tasks.…”