google/perfetto
Nov 2024 – Nov 2024
1 Month active
Languages Used
CC++
Technical Skills
Compiler InternalsDebuggingPerformance ProfilingRISC-V ArchitectureSystem Programming
Sung-hun Kim
PROFILE
Sung-hun Kim
Worked on stabilizing profiling reliability for RISC-V64 systems within the google/perfetto repository, focusing on resolving stack unwinding failures. Addressed a critical issue by aligning stack pointer retrieval with RISC-V64 calling conventions, which improved the accuracy and completeness of profiling data collection. Utilized expertise in compiler internals, debugging, and performance profiling, implementing the solution in C and C++. This targeted fix reduced data gaps and minimized the need for post-processing corrections, thereby enhancing platform support for RISC-V64. The work demonstrated a deep understanding of system programming and cross-architecture challenges, contributing to more robust profiling on emerging hardware platforms.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
93
Activity Months1
Your Network
433 peopleShared Repositories
248
Work History
November 2024
1 Commits
Nov 1, 2024In November 2024, focused on stabilizing and improving profiling reliability on RISC-V64 within google/perfetto. A targeted stack unwinding reliability improvement addressed unwinding failures on RISC-V64 by aligning stack pointer collection with the architecture's calling conventions, resulting in more reliable profiling data collection on RISC-V64 systems. The change was implemented and committed as 4d7d32df2a190e625d6d298b9a4b697d5a51c1c2.
1 Commits
Nov 1, 2024In November 2024, focused on stabilizing and improving profiling reliability on RISC-V64 within google/perfetto. A targeted stack unwinding reliability improvement addressed unwinding failures on RISC-V64 by aligning stack pointer collection with the architecture's calling conventions, resulting in more reliable profiling data collection on RISC-V64 systems. The change was implemented and committed as 4d7d32df2a190e625d6d298b9a4b697d5a51c1c2.
November 2024
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++
Technical Skills
Compiler InternalsDebuggingPerformance ProfilingRISC-V ArchitectureSystem Programming
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