On the finding proper cache prediction model using neural network
On the finding proper cache prediction model using neural network
复制标题
利用神经网络寻找合适的缓存预测模型
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Phongpensri
中科院分区：
文献类型：
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作者：
Songchok Khakhaeng;C. Phongpensri
Cache是计算机体系结构中的重要组成部分。它是一种非常接近CPU的存储器，可以快速访问。在当前技术下，该高速缓存价格仍然昂贵，因此大小不能很大。选择合适的该高速缓存设计，可以在保证程序快速执行的同时，节省硬件总成本。在本文中，我们应用数据挖掘技术，找到合适的缓存模型。我们特别考虑预测该高速缓存块大小。首先，描述了如何收集地址引用轨迹。几种工具被用来收集痕迹。我们对数据挖掘基准NUMineBench [4]的跟踪感兴趣。从迹线中分析参考图案。提取与块大小相关的模式。这些特征与模拟的轨迹行为一起被用来建立预测模型，即神经网络。该方法被认为是有效的，并可以扩展到考虑其他参数，如缓存容量，关联性等。
Cache is an important component in computer architecture. It is a kind of memories that is located very close to CPU and can be accessed fast. With the current technology, the cache price is still expensive and thus the size cannot be very large. With the proper selection of the cache design, one can save the total hardware cost while certainly gaining the fast execution of programs. In this paper, we apply the data mining technique to find the proper cache model. We particularly consider to predict the cache block size. First, how to collect the address reference traces is described. Several tools are used to collect the traces. We are interested in the trace of the data mining benchmark, NUMineBench[4]. From the traces, the reference patterns are analyzed. Patterns related to block size are extracted. These features together with the trace behaviors from the simulation are used to build the prediction model, i.e, neural network. The methodology is found to be effective and can be expanded to consider other parameters such as cache capacity, associativity etc.