CHAMPVis: Comparative Hierarchical Analysis of Microarchitectural Performance

Pentecost, Lillian; Gupta, Udit; Ngan, Elisa; Beyer, Johanna; Wei, Gu-Yeon; Brooks, David; Behrisch, Michael

doi:10.1109/protools49597.2019.00013

Cited by 2 publications

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Daisen: A Framework for Visualizing Detailed GPU Execution

Zhang

Mosallaei

Shah

et al. 2021

Computer Graphics Forum

View full text Add to dashboard Cite

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 at http://gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post‐study evaluation survey are available at http://osf.io/j5ghq.

show abstract

Daisen: A Framework for Visualizing Detailed GPU Execution

Zhang

Mosallaei

Shah

et al. 2021

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

Daisen: A Framework for Visualizing Detailed GPU Execution

Zhang¹,

Mosallaei²,

Shah³

et al. 2021

Preprint

View full text Add to dashboard Cite

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 at gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post-study evaluation survey are available at osf.io/j5ghq.

show abstract

CHAMPVis: Comparative Hierarchical Analysis of Microarchitectural Performance

Cited by 2 publications

References 15 publications

Daisen: A Framework for Visualizing Detailed GPU Execution

Daisen: A Framework for Visualizing Detailed GPU Execution

Daisen: A Framework for Visualizing Detailed GPU Execution

Contact Info

Product

Resources

About