intel/iaprof
Oct 2024 – Apr 2026
7 Months active
Languages Used
CC++PerlShellbashMarkdownBash
Technical Skills
Build SystemsCC programmingC++Data StructuresDebugging
Brandon Kammerdiener
PROFILE
Brandon Kammerdiener
Worked on the intel/iaprof repository, delivering features and fixes to enhance AI and GPU profiling workflows. Focused on profiling tool architecture, performance optimization, and build automation, the work included a major tool rework, LLVM-based C++ demangling, and robust ELF parsing for accurate symbol resolution. Improvements to flamegraph tooling, batch processing, and kernel tracing increased profiling reliability and scalability. Contributed documentation and automated scripts to streamline onboarding and setup, while addressing shader debugging and crash stability. Leveraged C, C++, and Bash, applying skills in low-level systems programming, performance analysis, and release management to improve maintainability and user experience.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
28Total
Bugs
5
Commits
28
Features
15
Lines of code
320,245
Activity Months7
Your Network
2115 peopleSame Organization
@intel.com2109
Shared Repositories
6
Brandon KammerdienerMember
Brendan GreggMember
Gabriel MunozMember
Ben OlsonMember
Ben OlsonMember
Robert DowerMember
Work History
April 2026
2 Commits • 2 Features
Apr 1, 2026April 2026 – Intel iaprof: Improved developer experience and setup efficiency. Delivered two features focused on documentation and automation: 1) Intel iaprof profiling documentation enhanced to clarify fast mode, prerequisites, frame pointer requirements, and interpretation of results; 2) Automated build/install script for the Intel Graphics stack to streamline cloning, building, and installing Intel Graphics Compiler, OpenCL Loader, and Compute Runtime. These changes reduce onboarding time, improve profiling accuracy interpretation, and accelerate graphics stack integration across hardware.
2 Commits • 2 Features
Apr 1, 2026April 2026 – Intel iaprof: Improved developer experience and setup efficiency. Delivered two features focused on documentation and automation: 1) Intel iaprof profiling documentation enhanced to clarify fast mode, prerequisites, frame pointer requirements, and interpretation of results; 2) Automated build/install script for the Intel Graphics stack to streamline cloning, building, and installing Intel Graphics Compiler, OpenCL Loader, and Compute Runtime. These changes reduce onboarding time, improve profiling accuracy interpretation, and accelerate graphics stack integration across hardware.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for intel/iaprof focusing on business value and technical achievements. The primary delivery this month was a substantial Profiling Tool Rework for AI and GPU profiling, with improved performance and usability, plus added configuration files, build scripts, and comprehensive documentation to support the new features and adoption. No major bugs fixed were reported for this repository this month. The work accelerates AI/GPU profiling workflows, improves maintainability, and positions the project for future enhancements. Demonstrated technologies include profiling tool architecture, performance optimization, build automation, configuration management, and clear technical documentation.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for intel/iaprof focusing on business value and technical achievements. The primary delivery this month was a substantial Profiling Tool Rework for AI and GPU profiling, with improved performance and usability, plus added configuration files, build scripts, and comprehensive documentation to support the new features and adoption. No major bugs fixed were reported for this repository this month. The work accelerates AI/GPU profiling workflows, improves maintainability, and positions the project for future enhancements. Demonstrated technologies include profiling tool architecture, performance optimization, build automation, configuration management, and clear technical documentation.
May 2025
1 Commits
May 1, 2025May 2025: Stabilized shader debugging workflows in intel/iaprof by delivering a targeted bug fix to the Shader Debug Collector and validating its impact on shader binary extraction. Corrected the offset calculation in extract_elf_shader_binary to derive the correct shader binary portion from the address, addressing assertion failures when multiple shaders share the same ELF section. This change reduces false negatives, shortens debugging cycles, and improves developer productivity in shader analysis. Commit reference: 1b77c627210407d50b780b67e384333868d9b385. Technologies exercised include C++, ELF binary analysis, shader pipelines, and Git-based traceability.
1 Commits
May 1, 2025May 2025: Stabilized shader debugging workflows in intel/iaprof by delivering a targeted bug fix to the Shader Debug Collector and validating its impact on shader binary extraction. Corrected the offset calculation in extract_elf_shader_binary to derive the correct shader binary portion from the address, addressing assertion failures when multiple shaders share the same ELF section. This change reduces false negatives, shortens debugging cycles, and improves developer productivity in shader analysis. Commit reference: 1b77c627210407d50b780b67e384333868d9b385. Technologies exercised include C++, ELF binary analysis, shader pipelines, and Git-based traceability.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 (intel/iaprof) — Focused on onboarding, setup reliability, and runtime stability. Delivered setup guidance and addressed a shader access crash, with documentation improvements to clarify build/execution steps. These changes enhance developer productivity, reduce onboarding friction, and improve runtime stability for end users.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 (intel/iaprof) — Focused on onboarding, setup reliability, and runtime stability. Delivered setup guidance and addressed a shader access crash, with documentation improvements to clarify build/execution steps. These changes enhance developer productivity, reduce onboarding friction, and improve runtime stability for end users.
March 2025
11 Commits • 6 Features
Mar 1, 2025March 2025 (2025-03) focused on modernizing the iaprof architecture, improving data fidelity, and enhancing tooling to support scalable performance analysis and secure practices. Key outcomes include shader binary lookup accuracy improvements, multi-collector support, and cleanup of legacy components, alongside deeper kernel-tracing capabilities and user-facing reliability enhancements.
11 Commits • 6 Features
Mar 1, 2025March 2025 (2025-03) focused on modernizing the iaprof architecture, improving data fidelity, and enhancing tooling to support scalable performance analysis and secure practices. Key outcomes include shader binary lookup accuracy improvements, multi-collector support, and cleanup of legacy components, alongside deeper kernel-tracing capabilities and user-facing reliability enhancements.
March 2025
February 2025
5 Commits • 2 Features
Feb 1, 2025In February 2025, the iaprof development effort delivered targeted improvements to stall handling, robustness for large batch processing, and GPU symbol resolution during debugging. The work increased profiling reliability, reduced risk of dropped events, and improved debugging outcomes for complex workloads across GPU processes.
February 2025
5 Commits • 2 Features
Feb 1, 2025In February 2025, the iaprof development effort delivered targeted improvements to stall handling, robustness for large batch processing, and GPU symbol resolution during debugging. The work increased profiling reliability, reduced risk of dropped events, and improved debugging outcomes for complex workloads across GPU processes.
October 2024
6 Commits • 3 Features
Oct 1, 2024October 2024 monthly summary for intel/iaprof focusing on reliability, profiling accuracy, and performance optimizations across demangling, ELF symbol handling, and flame-graph tooling. Delivered multiple features and bug fixes with clear business value and scalable technical improvements. Key features delivered: - C++ Demangling Integration using LLVM demangling library. Replaced libiberty with LLVM-based demangling, with build-system updates and a new C++ demangling source to improve reliability and native symbol handling. - Commit: 2c27dbf39afaf15b28d99f52709bdccdc08476a2 (Use LLVM to do C++ demangling.) - Flamegraph generation: CPython frame filtering option. Added a toggle to filter CPython frames from flame graphs; includes a --no-filter-cpython option to disable filtering and sed-based removal for cleaner profiling output. - Commit: bd3694fba90bc436ef40236bbc1d1662cf37e70d (Filter out CPython frames.) - Proto_flame storage optimization and visualization improvements. Moved storage from array to a hash map to enable efficient aggregation of stall counts per unique stack and reduce output size; reorganized flamegraph dependencies and added new color palettes for visualization. - Commits: - 741b1f4e3db4a5f9c6631c7c5900d0ad866e45f6 (Store proto_flames in a map that we can update counters for each round of eustalls; unique final stacks with total stall counts.) - 1e60127565a09d972295ef17f25608cf3833919a (fixups for creating release tarballs) - Proto_flame hashing correctness and API accessibility. Implemented missing equality for stack_hash() and inline proto_flame_equ in the header for better accessibility, ensuring robust hashing and comparisons for hash-table data structures. - Commit: 945d48afb9a06470b1f8eb6131636841c4bdcf6f (Fix forgotten implementatino of stack_hash()) - ELF symbol extraction robustness in i915 collector. Fixed parsing of ELF section names by stripping the leading '*. ' before adding as symbols to ensure accurate kernel name identification. - Commit: 94b48e4654d25e0bc4920973884676ac4d7565d4 (When getting kernel names from bare ELF section names, chop off *. prefix.) Major bugs fixed: - Corrected ELF section name parsing to ensure kernel-name extraction is accurate, preventing mislabeling of symbols during collection. - Fixed missing equality for stack_hash(), ensuring reliable hashing and stable hash-table behavior in proto_flame structures. Overall impact and accomplishments: - Increased profiling reliability and accuracy: LLVM-based C++ demangling provides better symbol resolution; CPython frame filtering offers flexible profiling visibility. - Improved scalability and performance of flame-graph outputs: hash-map storage eliminates duplicate stacks and dramatically reduces final output size, enabling efficient analysis of large traces. - Reduced risk and maintenance cost: simplified dependencies, clearer API accessibility, and robust hashing lead to more stable tooling and easier future enhancements. Technologies/skills demonstrated: - C++ build system integration and LLVM demangling usage for native symbol handling. - ELF parsing and kernel symbol extraction robustness. - Flamegraph tooling and profiling pipeline improvements, including CPython frame filtering and visualization color palettes. - Data structures and algorithms: hash maps for scalable aggregation, hash function correctness, and API access. - Release engineering: tarball release fixups and packaging considerations. Business value: - Faster, more reliable profiling translates to quicker issue diagnosis and performance optimization for customers relying on iaprof, while reducing artifact sizes and dependency surface.
6 Commits • 3 Features
Oct 1, 2024October 2024 monthly summary for intel/iaprof focusing on reliability, profiling accuracy, and performance optimizations across demangling, ELF symbol handling, and flame-graph tooling. Delivered multiple features and bug fixes with clear business value and scalable technical improvements. Key features delivered: - C++ Demangling Integration using LLVM demangling library. Replaced libiberty with LLVM-based demangling, with build-system updates and a new C++ demangling source to improve reliability and native symbol handling. - Commit: 2c27dbf39afaf15b28d99f52709bdccdc08476a2 (Use LLVM to do C++ demangling.) - Flamegraph generation: CPython frame filtering option. Added a toggle to filter CPython frames from flame graphs; includes a --no-filter-cpython option to disable filtering and sed-based removal for cleaner profiling output. - Commit: bd3694fba90bc436ef40236bbc1d1662cf37e70d (Filter out CPython frames.) - Proto_flame storage optimization and visualization improvements. Moved storage from array to a hash map to enable efficient aggregation of stall counts per unique stack and reduce output size; reorganized flamegraph dependencies and added new color palettes for visualization. - Commits: - 741b1f4e3db4a5f9c6631c7c5900d0ad866e45f6 (Store proto_flames in a map that we can update counters for each round of eustalls; unique final stacks with total stall counts.) - 1e60127565a09d972295ef17f25608cf3833919a (fixups for creating release tarballs) - Proto_flame hashing correctness and API accessibility. Implemented missing equality for stack_hash() and inline proto_flame_equ in the header for better accessibility, ensuring robust hashing and comparisons for hash-table data structures. - Commit: 945d48afb9a06470b1f8eb6131636841c4bdcf6f (Fix forgotten implementatino of stack_hash()) - ELF symbol extraction robustness in i915 collector. Fixed parsing of ELF section names by stripping the leading '*. ' before adding as symbols to ensure accurate kernel name identification. - Commit: 94b48e4654d25e0bc4920973884676ac4d7565d4 (When getting kernel names from bare ELF section names, chop off *. prefix.) Major bugs fixed: - Corrected ELF section name parsing to ensure kernel-name extraction is accurate, preventing mislabeling of symbols during collection. - Fixed missing equality for stack_hash(), ensuring reliable hashing and stable hash-table behavior in proto_flame structures. Overall impact and accomplishments: - Increased profiling reliability and accuracy: LLVM-based C++ demangling provides better symbol resolution; CPython frame filtering offers flexible profiling visibility. - Improved scalability and performance of flame-graph outputs: hash-map storage eliminates duplicate stacks and dramatically reduces final output size, enabling efficient analysis of large traces. - Reduced risk and maintenance cost: simplified dependencies, clearer API accessibility, and robust hashing lead to more stable tooling and easier future enhancements. Technologies/skills demonstrated: - C++ build system integration and LLVM demangling usage for native symbol handling. - ELF parsing and kernel symbol extraction robustness. - Flamegraph tooling and profiling pipeline improvements, including CPython frame filtering and visualization color palettes. - Data structures and algorithms: hash maps for scalable aggregation, hash function correctness, and API access. - Release engineering: tarball release fixups and packaging considerations. Business value: - Faster, more reliable profiling translates to quicker issue diagnosis and performance optimization for customers relying on iaprof, while reducing artifact sizes and dependency surface.
October 2024
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Quality Metrics
Correctness85.6%
Maintainability85.4%
Architecture84.2%
Performance78.6%
AI Usage21.4%
Skills & Technologies
Programming Languages
BashCC++MarkdownPerlShellbash
Technical Skills
AI profilingBPFBuild SystemsBuild automationCC ProgrammingC programmingC++C++ developmentC/C++ developmentCMakeCode RefactoringCommand Line InterfaceConcurrencyData Structures
Repositories Contributed To
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