ROCm/rdc
Dec 2024 – Dec 2024
1 Month active
Languages Used
C++Python
Technical Skills
GPU ComputingPerformance MonitoringSystem Libraries
Scaffidi, Salvatore
PROFILE
Scaffidi, Salvatore
Salvatore Scaffidi developed a new profiling metric for the ROCm/rdc repository, enabling runtime measurement of the ratio of cycles with an active warp on the streaming multiprocessor. He implemented this feature by extending the C++ system library, updating data models and headers, and integrating Python bindings to ensure accessibility across profiling workflows. This addition allows developers to observe and tune SM occupancy and warp scheduling with greater precision. Salvatore’s work demonstrated expertise in GPU computing and performance monitoring, delivering end-to-end traceability through a dedicated commit and enhancing the profiling toolchain without introducing new bugs or regressions during the release.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
5
Activity Months1
Your Network
1282 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly performance summary for ROCm/rdc focusing on profiling feature delivery and readiness for performance analysis. Key features delivered: - RDC Profiling: Introduced RDC_FI_PROF_SM_ACTIVE metric in the RDC library to measure the ratio of cycles with an active warp on the SM. This adds runtime observability to profiling workloads and enables more precise tuning of SM occupancy and warp scheduling. Major bugs fixed: - No major bugs reported this month for ROCm/rdc. (Note: no changes required beyond feature work.) Overall impact and accomplishments: - Expanded profiling capabilities across ROCm RDC with a new, actionable metric, enabling developers to quantify SM activity and optimize kernel performance. - Strengthened the profiling toolchain by updating definitions and bindings, ensuring the metric is accessible from C/C++ and Python profiling workflows. Technologies/skills demonstrated: - C/C++ library changes, data model extensions, header updates, and Python bindings integration. - End-to-end change traceability via a dedicated commit, improving reproducibility and review quality.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly performance summary for ROCm/rdc focusing on profiling feature delivery and readiness for performance analysis. Key features delivered: - RDC Profiling: Introduced RDC_FI_PROF_SM_ACTIVE metric in the RDC library to measure the ratio of cycles with an active warp on the SM. This adds runtime observability to profiling workloads and enables more precise tuning of SM occupancy and warp scheduling. Major bugs fixed: - No major bugs reported this month for ROCm/rdc. (Note: no changes required beyond feature work.) Overall impact and accomplishments: - Expanded profiling capabilities across ROCm RDC with a new, actionable metric, enabling developers to quantify SM activity and optimize kernel performance. - Strengthened the profiling toolchain by updating definitions and bindings, ensuring the metric is accessible from C/C++ and Python profiling workflows. Technologies/skills demonstrated: - C/C++ library changes, data model extensions, header updates, and Python bindings integration. - End-to-end change traceability via a dedicated commit, improving reproducibility and review quality.
December 2024
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
GPU ComputingPerformance MonitoringSystem Libraries
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