Machine Learned Machines: Adaptive co-optimization of caches, cores, and On-chip Network
Machine Learned Machines: Adaptive co-optimization of caches, cores, and On-chip Network
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机器学习机器：缓存、内核和片上网络的自适应协同优化
DOI：
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发表时间：
2016
期刊：
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通讯作者：
S. Subramoney
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作者：
Rahul Jain;P. Panda;S. Subramoney
现代多核架构需要运行时优化技术来解决不同进程的动态资源需求与运行时分配之间的不匹配问题。由于非加性效应，在运行时在多个优化之间进行选择是复杂的，这使得机器学习技术的自适应性非常有用。我们提出了一种新的方法，机器学习机（MLM），通过使用在线强化学习（RL）执行动态分区的最后一级缓存（LLC），沿着动态电压和频率缩放（DVFS）的核心和非核心（互连网络和LLC）。我们表明，协同优化的结果在更低的能量延迟产品（EDP）比任何单独应用的技术。结果显示，平均19.6%的EDP和2.6%的执行时间比基线改善。
Modern multicore architectures require runtime optimization techniques to address the problem of mismatches between the dynamic resource requirements of different processes and the runtime allocation. Choosing between multiple optimizations at runtime is complex due to the non-additive effects, making the adaptiveness of the machine learning techniques useful. We present a novel method, Machine Learned Machines (MLM), by using Online Reinforcement Learning (RL) to perform dynamic partitioning of the last level cache (LLC), along with dynamic voltage and frequency scaling (DVFS) of the core and uncore (interconnection network and LLC). We show that the co-optimization results in much lower energy-delay product (EDP) than any of the techniques applied individually. The results show an average of 19.6% EDP and 2.6% execution time improvement over the baseline.