intel/pti-gpu
Jan 2025 – Jan 2025
1 Month active
Languages Used
MarkdownPythonShell
Technical Skills
Code OrganizationData ProcessingDocumentationPerformance AnalysisScriptingTool Development
iermolae
PROFILE
Iermolae
Igor Ermolaev developed a GPU kernel categorization and JSON summary generation feature for the intel/pti-gpu repository, focusing on enhancing performance analysis workflows. He implemented Python scripts to process unitrace outputs, enabling automated categorization of GPU kernels and aggregation by user-defined schemas. By integrating these tools directly with unitrace, Igor streamlined the production of standardized JSON summaries, supporting higher-level insights into kernel behavior. His work included reorganizing schema files and merging documentation using Markdown, which improved onboarding for performance engineers. The project demonstrated depth in code organization, data processing, and scripting, addressing the need for more accessible and structured performance data.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
573
Activity Months1
Your Network
1985 people
Work History
January 2025
2 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — Intel/pti-gpu: Delivered GPU Kernel Categorization and JSON Summary Generation from UnitRace Outputs. Implemented Python tooling to categorize kernels from unitrace summaries, aggregate by category, and produce JSON summaries. Introduced support for user-defined categorization schemas, and integrated with unitrace for end-to-end analysis. Documentation merged with unitrace README and schema files reorganized to support the feature. This work enables standardized, higher-level insights into kernel behavior, accelerates performance analysis, and improves onboarding for performance engineers.
2 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — Intel/pti-gpu: Delivered GPU Kernel Categorization and JSON Summary Generation from UnitRace Outputs. Implemented Python tooling to categorize kernels from unitrace summaries, aggregate by category, and produce JSON summaries. Introduced support for user-defined categorization schemas, and integrated with unitrace for end-to-end analysis. Documentation merged with unitrace README and schema files reorganized to support the feature. This work enables standardized, higher-level insights into kernel behavior, accelerates performance analysis, and improves onboarding for performance engineers.
January 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
Code OrganizationData ProcessingDocumentationPerformance AnalysisScriptingTool Development
Repositories Contributed To
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