Data Object Scale Aware Rank-Level Memory Allocation

The main memory is organized as bank/rank/channel structure, which can be used to improve performance by exploiting parallelism and locality. The previous works have employed sub-ranking techniques to add more bank resource, or guided the bank partition among parallel running processes for isolating the memory interference. However, these methods ignore the interference problem when the memory system involves multiple ranks. In this paper, through an analysis on data layout, we find that program's data is inclined to cluster into a single rank because of the limited working set. This phenomenon results in the underutilized memory resource and system performance. We propose DSRA (data object scale aware rank-level memory allocation), which provides a software-only way to deal with this problem. Based on the cost of interference among objects, DSRA puts them into different ranks to avoid cluster. Meanwhile, with the information extracted by compiler and operating system, it requires no modification of application and underlying hardware. Measurement shows that DSRA, implementing in the Linux 2.6.32 kernel and running on two different types of processors, improves the performance of memory intensive NAS benchmark and SPEC CPU2000 by up to 16%(6.8% on average), with little effect on the cache miss rate.