Abstract

In the present paper, we propose RDGC, a reuse distance-based performance analysis approach for GPU cache hierarchy. RDGC models the thread-level parallelism in GPUs to generate appropriate cache reference sequence. Further, reuse distance analysis is extended to model the multi-partition/multi-port parallel caches and employed by RDGC to analyze GPU cache memories. RDGC can be utilized for architectural space exploration and parallel application development through providing hit ratios and transaction counts. The results of the present study demonstrate that the proposed model has an average error of 3.72

Keywords

MSC

68M20

Cite this article

@article{cai-4071,
  author  = {Mohsen Kiani and Amir Rajabzadeh},
  title   = {RDGC: A Reuse Distance-Based Approach to GPU Cache Performance Analysis},
  journal = {Computing and Informatics},
  volume  = {38},
  number  = {2},
  pages   = {421--453},
  year    = {2019},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2019_2_421}
}
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