柔性核芯片多处理器上的多任务工作负载调度

Divya Gulati, Changkyu Kim, S. Sethumadhavan, S. Keckler, D. Burger
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引用次数: 30

摘要

虽然技术趋势已经迎来了芯片多处理器(CMP)时代,但一个基本问题是每个核心的尺寸是多少。目前大多数商业设计都是对称cmp (SCMP),其中每个内核都是相同的,范围从简单的RISC处理器到复杂的乱序x86处理器。一些研究人员提出了由多种核组成的非对称cmp (ACMP)。虽然对于acmp来说不是问题,但这两种体系结构的固定性质使它们容易受到内核粒度与工作负载并行性之间不匹配的影响,从而导致执行效率低下。为了弥补这一缺陷,最近的研究提出了灵活核cmp (FCMP),它具有聚合多个小处理核心以形成更大逻辑处理器的能力。fcmp引入了一个新的资源分配和调度问题,它必须决定应该配置多少逻辑处理器,每个处理器应该有多强大,以及每个任务应该在哪里/何时运行。本文介绍了这一问题,描述了与之相关的挑战,并评估了适用于fcmp上多任务处理的算法。我们还评估了各种配置的静态核cmp,并将它们与用于各种多任务工作负载的fcmp进行了比较。
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Multitasking workload scheduling on flexible-core chip multiprocessors
While technology trends have ushered in the age of chip multiprocessors (CMP), a fundamental question is what size to make each core. Most current commercial designs are symmetric CMPs (SCMP) in which each core is identical and range from a simple RISC processor to a complex out-of-order x86 processor. Some researchers have proposed asymmetric CMPs (ACMP) consisting of multiple types of cores. While less of an issue for ACMPs, the fixed nature of both these architectures makes them vulnerable to mismatches between the granularity of the cores and the parallelism in the workload, which can cause inefficient execution. To remedy this weakness, recent research has proposed flexible-core CMPs (FCMP), which have the capability of aggregating multiple small processing cores to form larger logical processors. FCMPs introduce a new resource allocation and scheduling problem which must determine how many logical processors should be configured, how powerful each processor should be, and where/when each task should run. This paper introduces and motivates this problem, describes the challenges associated with it, and evaluates algorithms appropriate for multitasking on FCMPs. We also evaluate static-core CMPs of various configurations and compare them to FCMPs for various multitasking workloads.
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Meeting points: Using thread criticality to adapt multicore hardware to parallel regions COMIC: A coherent shared memory interface for cell BE Pangaea: A tightly-coupled IA32 heterogeneous chip multiprocessor Multi-mode energy management for multi-tier server clusters MCAMP: Communication optimization on Massively Parallel Machines with hierarchical scratch-pad memory
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