TLB-based Block-Grain Classification of Private Data

Sequential and parallel applications use most of the data as private in a multi-core system. Recent proposals made use of this observation to reduce the area of the coherence directories or the memory access latency. The driving force of these proposals is the classification of private/shared memory data. The effectiveness of these proposals depends on the number of detected private data. The existing proposals perform the private/shared classification at page granularity, leading to a noticeable amount of miss-classified memory blocks. We propose a mechanism that works on block granularity using the translation lookaside buffer (TLB) to make accurate detection of private data, which increases the effectiveness of proposals relying on a private/shared classification. Simulation results show that the block-grain approach obtains 17.0% more accessed private miss data than the page-grain approach, which translates to an improvement in system performance by 6.02% compared to a page-grain approach.