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Architectural Support for the Stream Execution Model on General-Purpose Processors

机译:通用处理器上的流执行模型的架构支持

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There has recently been much interest in stream processing, both in industry (e.g., Cell, NVIDIA G80, ATI R580) and academia (e.g., Stanford Merrimac, MIT RAW), with stream programs becoming increasingly popular for both media and more general-purpose computing. Although a special style of programming called stream programming is needed to target these stream architectures, huge performance benefits can be achieved. In this paper, we minimally add architectural features to commodity general-purpose processors (e.g., Intel/AMD) to efficiently support the stream execution model. We design the extensions to reuse existing components of the general-purpose processor hardware as much as possible by investigating low-cost modifications to the CPU caches, hardware prefetcher, and the execution core. With a less than 1% increase in die area along with judicious use of a software runtime system, we can efficiently support stream programming on traditional processor cores. We evaluate our techniques by running scientific applications on a cycle-level simulation system. The results show that our system executes stream programs as efficiently as possible, limited only by the ALU performance and the memory bandwidth needed to feed the ALUs.
机译:最近,在行业(例如Cell,NVIDIA G80,ATI R580)和学术界(例如Stanford Merrimac,MIT RAW)的流处理中都引起了人们的极大兴趣,流程序对于媒体和更通用的应用都越来越受欢迎计算。尽管需要一种称为流编程的特殊编程风格来针对这些流体系结构,但是可以实现巨大的性能优势。在本文中,我们将架构功能最少添加到商品通用处理器(例如Intel / AMD)中,以有效支持流执行模型。通过研究对CPU高速缓存,硬件预取器和执行内核的低成本修改,我们设计了扩展,以尽可能多地重用通用处理器硬件的现有组件。通过适当地使用软件运行时系统,芯片面积增加不到1%,我们可以有效地支持传统处理器内核上的流编程。我们通过在周期级仿真系统上运行科学应用程序来评估我们的技术。结果表明,我们的系统尽可能高效地执行流程序,仅受ALU性能和馈送ALU所需的内存带宽的限制。

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