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

机译:对并行和异构平台上动态流任务图的节能执行的代码生成

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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.
机译:流式任务图是用于在数据流上运行的并行应用的高级规范。对于静态任务图结构,静态调度器可用于将任务映射到并行平台上,以最小化给定吞吐量的能量消耗。我们将动态元素介绍到任务图结构中,从而指定了在运行时调整行为的应用程序,例如,从检查到活动模式切换。这反过来需要一个运行时系统,可以重新映射任务并在任务结构的动态变化的情况下潜在地调整它们的并行度。我们提供了一个工具链,并通过综合性和实际应用来评估我们的原型。我们发现,与基于整数线性规划的最佳静态调度器相比,我们符合吞吐量要求,平均符合吞吐量要求。重新绘制的运行时开销可以忽略不计,并且准确预测应用程序运行时和能量。我们还概述了如何将系统扩展到异构平台。

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