EXCEEDS logo
Exceeds
adyabr

PROFILE

Adyabr

Aditya Yadav contributed to the google/perfetto repository by developing advanced performance diagnostics and jank classification features for frame rendering analysis. He implemented new jank types to capture display mode and power transitions, enhanced the frame timeline with experimental toggles and side-by-side visualizations, and introduced fence latching state tracking for deeper GPU synchronization insights. Using C++, Python, and SQL, Aditya improved data modeling and debugging workflows, enabling more accurate and granular performance metrics. His work addressed real-world issues such as mislabeling of janky frames and reduced noise in reports, demonstrating a thoughtful, iterative approach to system tracing and optimization.

Overall Statistics

Feature vs Bugs

83%Features

Repository Contributions

10Total
Bugs
1
Commits
10
Features
5
Lines of code
625
Activity Months6

Work History

April 2026

1 Commits • 1 Features

Apr 1, 2026

April 2026: Delivered Frame Timeline Fence Latching State Tracking in google/perfetto. Added new enumerations and optional fields to capture latched fence states and counts of unsignaled frames, enabling deeper performance analysis and more accurate frame pacing diagnostics. This change, tied to commit 199147ace586938a7d18c1784ce87e012340dfa7 and linked to bug 492301019, strengthens observability of GPU fencing and synchronization within the frame timeline.

February 2026

1 Commits • 1 Features

Feb 1, 2026

February 2026 monthly summary for google/perfetto focusing on performance instrumentation enhancements and jank taxonomy improvements. Delivered a new jank type for Display Power Mode Changes and grouped non-perceivable jank tags to reduce noise in performance reports, enabling clearer power-transition analysis and faster debugging.

January 2026

1 Commits • 1 Features

Jan 1, 2026

January 2026: Delivered a targeted performance diagnostics enhancement for Perfetto by introducing a new jank type to track display mode changes within the frame timeline. This enables clearer reporting of performance impact during display mode transitions and supports learning of new vsync cadence. The change is tied to commit 53beba68daf906ad25592bb6480248f3fb23d5e1 and addresses bug 465786055, improving observability and debugging efficiency for UI rendering paths.

December 2025

2 Commits • 1 Features

Dec 1, 2025

December 2025 monthly summary for google/perfetto focusing on key accomplishments, business value, and technical achievements. Key features delivered: - Enhanced jank classification and debugging for frame rendering: improved jank tagging in the frame timeline event parser for SurfaceFlinger Scheduling and added a debug field for jank classification, enabling more precise performance analysis. Major bugs fixed: - Fixed mislabeling of janky frames due to missing handling of the SurfaceFlinger Scheduling jank type and SurfaceFrames SF stuffing, ensuring accurate jank tagging and debugging visibility. Overall impact and accomplishments: - Improved accuracy of frame rendering performance metrics, enabling faster root-cause analysis and optimization across devices; reduced time to diagnose jank-related issues; strengthened cross-team collaboration with Android SurfaceFlinger performance work. Technologies/skills demonstrated: - Performance tracing and frame timeline parsing in Perfetto, jank severity scoring, added debug instrumentation, and close collaboration with Android SurfaceFlinger teams. Notable commits: - fca800884a32a9d79d299fd493d753301c6aeaf6: SurfaceFrames should handle SF stuffing (#3931) - fixes jank handling for SurfaceFlinger Scheduling frames - 594fdf91ce120f3dbac71c88852b75b93527ecad: perfetto: Add a debug field to reason about jank classification (#4124) - adds debug field and jank severity score calculation

October 2025

4 Commits • 1 Features

Oct 1, 2025

Monthly summary for 2025-10 (google/perfetto): Key features delivered: - Experimental Jank Classification and Visualization Enhancements for FrameTimeline: delivered a consolidated feature group enabling experimental jank measurement fields, a user toggle to switch between traditional and experimental classification, an experimental side-by-side comparison track, and a new jank type JANK_DISPLAY_NOT_ON to capture jank when the display is not active. Major bugs fixed: - No critical bugs reported this month. Efforts focused on feature rollout and data consistency improvements, including robust handling for display-off scenarios. Overall impact and accomplishments: - Strengthened frame timing analytics with richer data fields and flexible classification, enabling earlier detection of performance regressions and improved diagnosis of jank. - Provided users with an adjustable experimentation pathway (feature toggle) and visual aids (side-by-side track) to compare classification approaches. - Improved reporting granularity for non-active display states, enhancing accuracy of performance measurements during idle periods. Technologies/skills demonstrated: - Data modeling and instrumentation for FrameTimeline in Perfetto; experimentation via feature toggles; UI/visualization enhancements; commit-driven development; cross-functional collaboration via incremental commits. Commits (representative): - c63922cabbde52bacdd51a17ea6cc472ca5aceb3: Add a few additional fields to Frametimeline, currently in experimental mode (#3159) - f1469e753c78b52cbb4fe1abb82141ac82903bc0: Add a settings toggle for experimental jank classification (#3180) - 69bd8a4b80aca14a9f07b5443572bb34150e496c: Show an additional track for experimental jank classification for a side-by-side comparison (#3184) - 9e88d7f37407ab04dc3e158d6b8673a06014430f: Add a new jank type for when the display is not ON (#3356)

March 2021

1 Commits

Mar 1, 2021

March 2021 monthly summary for google/android-cuttlefish: Graphics rendering stability improved by reverting ANGLE and adopting SwiftShader as the native OpenGL driver for Cuttlefish. Focused on delivering a stable OpenGL path and reducing rendering issues for GL-based applications in the emulator.

Activity

Loading activity data...

Quality Metrics

Correctness89.0%
Maintainability87.0%
Architecture89.0%
Performance82.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++PythonSQLTypeScriptcccppprotopyts

Technical Skills

C++C++ developmentDebuggingExperimental Feature ImplementationFeature TogglingFrontend DevelopmentPerformance AnalysisProtocol BuffersPython scriptingSQLSystem TracingTrace ProcessingUI Developmentdata analysisdata modeling

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

google/perfetto

Oct 2025 Apr 2026
5 Months active

Languages Used

C++PythonSQLTypeScriptccprotopyts

Technical Skills

DebuggingExperimental Feature ImplementationFeature TogglingFrontend DevelopmentPerformance AnalysisProtocol Buffers

google/android-cuttlefish

Mar 2021 Mar 2021
1 Month active

Languages Used

C++

Technical Skills

C++graphics programmingsystem programming