Online concurrent workload classification for multi-core energy management
Online concurrent workload classification for multi-core energy management
复制标题
多核能源管理的在线并发工作负载分类
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Singh
中科院分区：
文献类型：
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作者：
B. K. Reddy;G. Merrett;B. Al;A. Singh
现代嵌入式多核处理器被组织为核集群，其中每个集群中的所有核都以共同的电压频率（V−f）工作。这些处理器通常需要同时执行应用程序，根据指令混合和资源共享表现出不同和混合的工作负载（例如计算和内存密集型）。自适应是实现节能的关键，而无需在应用程序性能与此类工作负载变化之间进行权衡。在本文中，我们提出了一种在线能源管理技术，执行并发工作负载分类使用度量内存读取每指令（MRPI），并通过工作负载预测主动选择一个适当的V−f设置。随后，它会监控工作负载预测误差和性能损失（在运行时通过每秒指令数（IPS）量化），并调整所选的V−f进行补偿。我们验证了所提出的技术Odroid XU3与各种组合的基准应用程序。结果表明，与现有方法相比，能源效率提高了69%。
Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (V−f). Such processors often need to execute applications concurrently, exhibiting varying and mixed workloads (e.g. compute- and memory-intensive) depending on the instruction mix and resource sharing. Runtime adaptation is key to achieving energy savings without trading-off application performance with such workload variabilities. In this paper, we propose an online energy management technique that performs concurrent workload classification using the metric Memory Reads Per Instruction (MRPI) and pro-actively selects an appropriate V−f setting through workload prediction. Subsequently, it monitors the workload prediction error and performance loss, quantified by Instructions Per Second (IPS) at runtime and adjusts the chosen V−f to compensate. We validate the proposed technique on an Odroid-XU3 with various combinations of benchmark applications. Results show an improvement in energy efficiency of up to 69% compared to existing approaches.