Modern architectures track memory accesses using page granularity metadata such as access and dirty bits, leading to fundamental tradeoffs for system software that uses this metadata. Larger page sizes reduce address translation overheads and page table footprints. However, coarse metadata bits for larger pages limit software’s visibility into application-level memory usage, resulting in memory bloat and performance pathologies. As DRAM capacity continues to expand, we expect software to react by aggressively mapping with larger page sizes, making this tradeoff space more challenging to navigate.

We study the relationship between metadata granularity and fidelity, the degree to which metadata correctly approximates actual access patterns. We focus on 2MB page support on x86-64 and GPUs, measuring fidelity across a wide range of benchmarks. Fidelity can be poor at a coarse granularity, and high variance occurs even within applications. To address this problem, we propose Prism, which provides architectural support for variable–granularity access and dirty bits. Evaluation of Linux/x86-64 and GPU prototypes of Prism show modest additional hardware can reduce metadata fidelity loss by up to 65% and 55% at a performance cost of less than 1% and 2% on CPUs and GPUs respectively. We show that the recovered fidelity can eliminate performance pathologies and improve the performance of GPGPU applications using demand paging by 29.8% on average.



October, 2020


Research Areas

  • Computer Architecture
  • Virtual Memory




International Conference on Parallel Architectures and Compilation Techniques (PACT)