RDGC: A Reuse Distance-Based Approach to GPU Cache Performance Analysis

Authors

  • Mohsen Kiani Department of Computer Engineering and Information Technology, Engineering Faculty, Razi University, Taghe-Bostan, Kermanshah, Iran
  • Amir Rajabzadeh Department of Computer Engineering and Information Technology, Engineering Faculty, Razi University, Taghe-Bostan, Kermanshah, Iran

DOI:

https://doi.org/10.31577/cai_2019_2_421

Keywords:

GPU cache memory, reuse distance analysis, performance modeling, hit ratio

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 % and 4.5 % (for L1 and L2 hit ratios, respectively). The results also indicate that the slowdown of RDGC is equal to 47 000 times compared to hardware execution, while it is 59 times faster than GPGPU-Sim simulator.

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Published

2019-05-31

How to Cite

Kiani, M., & Rajabzadeh, A. (2019). RDGC: A Reuse Distance-Based Approach to GPU Cache Performance Analysis. COMPUTING AND INFORMATICS, 38(2), 421–453. https://doi.org/10.31577/cai_2019_2_421