GPU Performance Analysis Interface for Bottleneck Identification
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Solution Overview
Problem
Conventional performance tools for GPUs are inadequate in identifying bottlenecking and underutilized units within graphical pipelines, leading to inefficient optimization and performance enhancement, as they provide limited and misleading data, especially with modern multi-pipeline architectures.
Innovation Solution
A computer-implemented user interface and method for graphical processing analysis that allows for the identification of bottlenecking and underutilized units by providing detailed performance data at the graphical frame and operation level, enabling quick comparison and granular analysis of GPU performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional performance tools are used to analyze GPU pipeline performance, then the analysis process is simplified, but the measurement precision and reliability of bottleneck identification deteriorates due to limited and misleading data
Solution Approach 1:
The patent segments the GPU pipeline analysis into multiple hierarchical levels: pipeline stage level, operational primitive level, and individual unit level. This segmentation allows the system to maintain ease of operation by providing structured analysis while improving measurement precision through granular performance data collection at each segment, enabling accurate bottleneck identification without overwhelming the user.
Solution Approach 2:
The patent introduces a new dimension of analysis by providing multi-level hierarchical data organization. Instead of presenting flat averaged data, the system creates multiple layers of performance information from pipeline stages down to individual operational primitives, allowing users to navigate from high-level overview to detailed analysis, thereby improving both ease of operation and measurement precision simultaneously.
2Device complexity
If conventional performance tools provide averaged and scrolling data, then the device complexity is reduced, but the ability to perform granular GPU analysis at the graphical frame and operation level deteriorates
Solution Approach 1:
The patent implements dynamic data presentation that adapts to user needs. The system can dynamically switch between aggregated views and granular details, allowing users to drill down from pipeline-stage-level summaries to operational-primitive-level specifics. This dynamic adaptability maintains manageable device complexity while providing the granularity needed for precise performance analysis when required.
Solution Approach 2:
The patent employs a nested hierarchical structure where pipeline stage data contains operational primitive data, which in turn contains individual unit performance data. This nesting allows the system to present simplified aggregated views at higher levels while preserving detailed granular information at lower levels, effectively managing device complexity while enabling precise analysis through selective navigation into nested layers.
3Ease of operation
If conventional tools display only average performance data, then the ease of operation is improved, but the productivity of GPU optimization deteriorates due to inability to identify specific bottlenecking units
Solution Approach 1:
The patent performs preliminary action by pre-organizing performance data into a hierarchical structure before presentation. The system预先 collects and structures data at multiple levels (pipeline stages, operational primitives, individual units) so that when users need detailed analysis, the information is already prepared and organized. This preliminary organization maintains ease of operation for overview while enabling rapid productive optimization when detailed bottleneck identification is needed.
Solution Approach 2:
The patent implements feedback mechanisms that provide users with actionable insights at multiple levels of detail. The system monitors performance and provides feedback that can range from high-level pipeline efficiency metrics to specific unit-level bottleneck identification. This multi-level feedback approach maintains simplicity for general monitoring while enabling productive optimization through detailed feedback when users drill down into specific performance issues.
4Device complexity
If conventional performance tools lack detailed performance data, then the device complexity is reduced, but the ability to provide targeted performance enhancements deteriorates
Solution Approach 1:
The patent segments performance data collection into distinct hierarchical levels, collecting data at pipeline stage level, operational primitive level, and individual unit level. This segmentation allows the system to manage device complexity by organizing data collection in structured modules while simultaneously improving productivity through the availability of detailed granular data needed for targeted performance enhancements and precise bottleneck identification.
Data Source
AI summary
A computer-implemented user interface and method for graphical processing analysis. More specifically, embodiments provide a convenient and effective mechanism for presenting GPU performance information such that one or more bottlenecking and/or underutilized graphics pipeline units may be identified. The presentation of the information enables quick comparison of all graphical operations within a frame for analysis with increased granularity. Additionally, the performance of graphical operations with common state attributes may be compared to more effectively and efficiently enhance GPU performance.