24833

Daisen: A Framework for Visualizing Detailed GPU Execution

Yifan Sun, Yixuan Zhang, Ali Mosallaei, Michael D. Shah, Cody Dunne, David Kaeli
William & Mary, Williamsburg, Virginia
arXiv:2104.00828 [cs.DC], (2 Apr 2021)

@misc{sun2021daisen,

   title={Daisen: A Framework for Visualizing Detailed GPU Execution},

   author={Yifan Sun and Yixuan Zhang and Ali Mosallaei and Michael D. Shah and Cody Dunne and David Kaeli},

   year={2021},

   eprint={2104.00828},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

Download Download (PDF)   View View   Source Source   

389

views

Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator-generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator-generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator-generated GPU execution traces. Daisen also includes a web-based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open-sourced implementation of Daisen can be found. Supplemental materials including a demo video, survey questions, evaluation study guide, and post-study evaluation survey are available.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2021 hgpu.org

All rights belong to the respective authors

Contact us: