Classifying Data Blocks at Subpage Granularity with an On-Chip Page Table to Improve Coherence in Tiled CMPs

As shown in some prior studies, a significant percentage of data blocks accessed in parallel codes are private, and not keeping track of those blocks can improve the effectiveness of directory structures in CMPs. In this study, we have two major contributions. First, we showed that compared to the classification of cache blocks at page granularity, data block classification at subpage level helps to detect considerably more private data blocks. Based on this idea, we propose two different approaches for enhancing the effectiveness of directory caches in tiled CMPs. In the first approach, which is called Quasi-Dynamic subpage level data Block Classification (QDBC), a data block is assumed to be private from the beginning of the program execution and stays private as long as the corresponding subpage is accessed by only one core. Our second approach, which is called Dynamic subpage level data Block Classification (DBC), turns a data block into private again after all blocks within the corresponding subpage are evicted from private cache hierarchy. Memory block classification at subpage level, however, may increase the frequency of the operating system involvement in updating the maintenance bits in page table entries. To overcome this, we propose, as a second contribution, a distributed table called as on-chip page table, which stores recently accessed page translations in the system. Our simulation results show that, compared to page level data classification, QDBC and DBC approaches relying on the on-chip page table can detect significantly more private data blocks and considerably improve system performance. IEE