Data layout optimization based on the spatio-temporal model of field access

Yongliang Wang, Naijie Gu, Junjie Su, Dongsheng Qi, Zhuorui Ning
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Abstract

Memory access latency is one of the performance bottlenecks of most programs. Improving cache utilization is a common way to improve memory performance. Data layout optimization can improve cache performance based on the locality principle of the memory hierarchy. By analyzing the timestamp information and spatial information of memory access, a field access spatio-temporal model named FASTM was constructed to optimize the data layout of the structure. FASTM consists of three parts: hot data analysis, relative memory access count model and memory access behavior similarity model. A heuristic algorithm based on FASTM is proposed to design the split optimization scheme of the structure. Experimental results on eight benchmarks from SPEC and Olden show that FASTM can reduce cache misses by 57.85% and Translation Lookaside Buffer (TLB) misses by 74.70% on average. The average speedup of program running time is up to 1.37x.
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基于野外存取时空模型的数据布局优化
内存访问延迟是大多数程序的性能瓶颈之一。提高缓存利用率是提高内存性能的常用方法。数据布局优化可以根据内存层次结构的局部性原则提高缓存性能。通过分析存储器访问的时间戳信息和空间信息,构建了一个场访问时空模型FASTM,对结构的数据布局进行优化。FASTM由热数据分析、相对内存访问数模型和内存访问行为相似度模型三部分组成。提出了一种基于FASTM的启发式算法来设计结构的拆分优化方案。在SPEC和Olden的8个基准测试上的实验结果表明,FASTM平均可以减少57.85%的缓存丢失和74.70%的转换Lookaside Buffer (TLB)丢失。程序运行时间的平均加速高达1.37倍。
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