.png){kind=icon}
返回
GPGPU Performance Estimation With Core and Memory Frequency Scaling
.png){kind=icon}
OA
AI
DOI：10.1109/TPDS.2020.3004623.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Contemporary graphics processing units (GPUs) support dynamic voltage and frequency scaling to balance computational performance and energy consumption. However, accurate and straightforward performance estimation for a given GPU kernel under different frequency settings is still lacking for real hardware, which is essential to determine the best frequency configuration for energy saving. In this article, we reveal a fine-grained analytical model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Compared to the cycle-level simulators, which are too slow to apply on real hardware, our model only needs simple and one-off micro-benchmarks to extract a set of hardware parameters and kernel performance counters without any source code analysis. Our experimental results show that the proposed performance model can capture the kernel performance scaling behaviors under different frequency settings and achieve decent accuracy (average errors of 3.85, 8.6, 8.82, and 8.83 percent on a set of 20 GPU kernels with four modern Nvidia GPUs).
Keyword:
Graphics processing units
dynamic voltage and frequency scaling
GPU performance modeling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
5.2K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容