Predictive coordination of multiple on-chip resources for chip multiprocessors
Predictive coordination of multiple on-chip resources for chip multiprocessors
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DOI：
10.1145/1995896.1995927
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
Jing Chen;L. John
中科院分区：
文献类型：
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作者：
Jing Chen;L. John
有效的片上资源管理是片上多处理器（CMP）实现高资源利用率和系统级性能目标的关键。现有的多资源管理方案要么集中于核心内资源，要么集中于核心间资源，错过了利用这两个级别的资源之间的相互作用的机会。此外，这些资源管理方案要么依赖于试运行或复杂的在线机器学习模型来搜索适当的资源分配，这使得资源管理效率低下且昂贵。为了解决这些局限性，本文提出了一种预测性，但成本效益的机制，多资源管理CMP。它使用一组硬件高效的在线分析器和分析性能模型来预测应用程序在不同内核内和/或内核间资源分配情况下的性能。基于预测的性能，资源分配器在没有任何试运行的情况下为每个时期识别并实施接近最优的资源分区。实验结果表明，该预测资源管理框架与等分区方案相比，平均提高了CMP系统的加权加速比11.6%，与现有的反应式资源管理方案相比，平均提高了9.3%。
Efficient on-chip resource management is crucial for Chip Multiprocessors (CMP) to achieve high resource utilization and enforce system-level performance objectives. Existing multiple resource management schemes either focus on intra-core resources or inter-core resources, missing the opportunity for exploiting the interaction between these two level resources. Moreover, these resource management schemes either rely on trial runs or complex on-line machine learning model to search for the appropriate resource allocation, which makes resource management inefficient and expensive. To address these limitations, this paper presents a predictive yet cost effective mechanism for multiple resource management in CMP. It uses a set of hardware-efficient online profilers and an analytical performance model to predict the application's performance with different intra-core and/or inter-core resource allocations. Based on the predicted performance, the resource allocator identifies and enforces near optimum resource partitions for each epoch without any trial runs. The experimental results show that the proposed predictive resource management framework could improve the weighted speedup of the CMP system by an average of 11.6% compared with the equal partition scheme, and 9.3% compared with existing reactive resource management scheme.