DeepAI AI Chat
Log In Sign Up

Daisen: A Framework for Visualizing Detailed GPU Execution

by   Yifan Sun, et al.

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 Supplemental materials including a demo video, survey questions, evaluation study guide, and post-study evaluation survey are available at


page 1

page 6

page 9


MGSim + MGMark: A Framework for Multi-GPU System Research

The rapidly growing popularity and scale of data-parallel workloads dema...

Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in ide...

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

To address the challenge of performance analysis on the US DOE's forthco...

Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool

Because of the limits input/output systems currently impose on high-perf...

TraceTracker: Hardware/Software Co-Evaluation for Large-Scale I/O Workload Reconstruction

Block traces are widely used for system studies, model verifications, an...

Exploring Modern GPU Memory System Design Challenges through Accurate Modeling

This paper explores the impact of simulator accuracy on architecture des...

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) br...