CSR: Medium: Supporting Real-Time Data Flows on Heterogeneous Multicore Platforms

该项目解决了在异构多核平台上实现软件的挑战，特别是在需要验证时间正确性的应用程序领域。多核革命目前正在经历一波以异构处理元素形式出现的创新浪潮——处理元素可能在功能或处理速度方面有所不同。这种异构性的存在意味着在将硬件资源分配给软件组件时必须做出选择。解决这种选择的需要增加了资源分配的复杂性，并且抑制了嵌入式计算行业采用这种平台，尽管在平衡性能和能源使用需求方面有很大的潜在好处。该项目将通过系统地检查不同的异构模型，为每个这样的模型设计有效的实时调度算法，并为每个模型获得适当的分析技术来验证实时约束，从而开发在异构平台上实现时间关键型软件的方法。工作负载将被建模为能够表示不同程度的并行性的数据流，并考虑了抢占性、处理器间迁移等约束的各种可能组合。该项目既解决了资源分配理论中的基本科学挑战，又寻求开发适当的软件工具和方法，以促进所获得的结果和见解的采用。这样的结果、工具和方法有望加速嵌入式系统行业采用先进的异构多核平台。

This project addresses the challenges of implementing software upon heterogeneous multicore platforms, particularly in application domains where verified timing correctness is required. The multicore revolution is currently undergoing a wave of innovation in the form of heterogeneous processing elements -- processing elements that may differ with respect to functionality or processing speed. The presence of such heterogeneity means that choices must be made when allocating hardware resources to software components. The need to resolve such choices adds considerable complexity to resource allocation, and inhibits the adoption of such platforms by the embedded computing industry despite significant potential benefits in terms of balancing performance and energy usage requirements.This project will develop methodologies for implementing time-critical software upon heterogeneous platforms by systematically examining different models of heterogeneity, devising efficient real-time scheduling algorithms for each such model, and obtaining appropriate analysis techniques for each model that enable real-time constraints to be verified. Workloads will be modeled as data-flows that enable the representation of varying degrees of parallelism, with various possible combinations of constraints on preemptivity, inter-processor migration, et cetera, considered.This project both addresses fundamental scientific challenges in resource allocation theory, and seeks to develop appropriate software tools and methodologies to facilitate the adoption of results and insights that are obtained. Such results, tools, and methodologies are expected to accelerate the adoption of advanced heterogenous multicore platforms by embedded systems industries.