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GCoM: a detailed GPU core model for accurate analytical modeling of modern GPUs

Jounghoo Lee, Yeonan Ha, Suhyun Lee, Jinyoung Woo, Jinho Lee, Hanhwi Jang, Youngsok Kim

2022Year
25Citations
5Top-tier citations

Abstract

Analytical models can greatly help computer architects perform orders of magnitude faster early-stage design space exploration than using cycle-level simulators. To facilitate rapid design space exploration for graphics processing units (GPUs), prior studies have proposed GPU analytical models which capture first-order stall events causing performance degradation; however, the existing analytical models cannot accurately model modern GPUs due to their outdated and highly abstract GPU core microarchitecture assumptions. Therefore, to accurately evaluate the performance of modern GPUs, we need a new GPU analytical model which accurately captures the stall events incurred by the significant changes in the core microarchitectures of modern GPUs.

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