ROCm/rdc
Dec 2024 – Dec 2024
1 Month active
Languages Used
C++Python
Technical Skills
GPU ComputingPerformance MonitoringSystem Libraries
Scaffidi, Salvatore
PROFILE
Scaffidi, Salvatore
Worked on the ROCm/rdc repository to expand profiling capabilities by introducing the RDC_FI_PROF_SM_ACTIVE metric, which quantifies the ratio of cycles with an active warp on the streaming multiprocessor. This feature enhances runtime observability for GPU workloads, enabling more precise performance analysis and kernel optimization. The implementation involved C++ library modifications, updates to data models and headers, and integration with Python bindings to ensure accessibility across profiling workflows. All changes were delivered with full traceability through a dedicated commit, reflecting a methodical approach to reproducibility and review. No major bugs were reported or addressed during this development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
5
Activity Months1
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1563 peopleSame Organization
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Shared Repositories
3
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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