Efficient classification of private memory blocks

Abstract Shared memory architectures are pervasive in the multicore technology era. Still, sequential and parallel applications use most of the data as private in a multicore system. Recent proposals using this observation and driven by a classification of private/shared memory data can reduce the coherence directory area or the memory access latency. The effectiveness of these proposals depends on the accuracy of the classification. The existing proposals perform the private/shared classification at page granularity, leading to a miss-classification and reducing the number of detected private memory blocks. We propose a mechanism able to accurately classify memory blocks using the existing translation lookaside buffers (TLB), which increases the effectiveness of proposals relying on a private/shared classification. Our experimental results show that the proposed scheme reduces L1 cache misses by 25% compared to a page-grain classification approach, which translates into an improvement in system performance by 8.0% with respect to a page-grain approach.