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Inter and intra kernel reuse analysis driven pipelining on Chip-Multiprocessors

机译:内部和内部内核重用分析驱动的多芯片处理器流水线

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As the demand for low power multimedia systems continues to grow, so will the need for low cost and efficient solutions. Driven by such need, as well as improvements IC design technology, Chip-Multiprocessors (CMPs) have emerged as a potential solution. CMPs offer flexibility, low cost, low power and the ability to handle highly parallel workloads. As CMPs scale, it is up to the designer to take full advantage of their computational resources and manage their constrained memory resources efficiently. In this paper we propose a methodology that enables designers to fully exploit the target platform's computational resources without sacrificing power consumption by maximizing the application's reuse. Our approach uses code transformations to split the application's tasks into smaller units of computations or subtasks called kernels. Each kernel is analyzed for inter and intra reuse opportunities in order to minimize unnecessary data transfers between kernels. Our approach also couples both scheduling/pipelining of tasks with their memory allocations. This allows us to obtain memory aware pipelined schedules that increases throughput and reduces power consumption. Our methodology has shown up to 15% performance improvements as well as 33% power reduction when compared to state of the art techniques.
机译:随着对低功耗多媒体系统的需求不断增长,对低成本和高效解决方案的需求也将不断增长。在这种需求以及改进的IC设计技术的推动下,芯片多处理器(CMP)已经成为一种潜在的解决方案。 CMP具有灵活性,低成本,低功耗以及处理高度并行工作负载的能力。随着CMP规模的扩大,设计人员应充分利用其计算资源并有效地管理其受限的内存资源。在本文中,我们提出了一种方法,使设计人员能够通过最大程度地利用应用程序的重用性,在不牺牲功耗的情况下充分利用目标平台的计算资源。我们的方法使用代码转换将应用程序的任务拆分为较小的计算单元或称为内核的子任务。分析每个内核的内部和内部重用机会,以最大程度地减少内核之间不必要的数据传输。我们的方法还将任务的调度/流水线与内存分配结合在一起。这使我们可以获得内存感知的流水线调度,从而提高吞吐量并降低功耗。与最先进的技术相比,我们的方法显示出高达15%的性能提升以及33%的功耗降低。

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