
Over six months, Marantic contributed to the ROCm/rocm-systems repository by developing and refining profiling and performance analysis tools for GPU workloads. He enhanced ROCProfiler with improved counter event handling and integrated PMC data, enabling more accurate performance metrics. Using C++ and CMake, he unified Perfetto tracing, optimized memory usage, and introduced MPI-aware trace merging for multi-rank profiling. Marantic also improved database integration with SQLite3, strengthened data validation, and expanded documentation to support onboarding. His work focused on maintainability, reliability, and usability, resulting in a more robust profiling stack that accelerates root-cause analysis and supports data-driven optimization decisions.
March 2026 milestones for ROCm rocm-systems focused on maintainability, reliability, and developer onboarding. Key features delivered include maintenance and simplification of internal profiling tooling for MPI trace merging and ROCprof availability, resulting in reduced binary footprint and faster startup; robustness enhancements for profiling metrics by aligning CPU sample scaling with established implementations and strengthening GPU metrics validation; and expanded user onboarding with comprehensive documentation and standalone build capabilities for rocprofiler-systems examples. These changes improve maintainability, reduce risk in production deployments, and provide clearer performance insights across the ROCm profiling stack.
March 2026 milestones for ROCm rocm-systems focused on maintainability, reliability, and developer onboarding. Key features delivered include maintenance and simplification of internal profiling tooling for MPI trace merging and ROCprof availability, resulting in reduced binary footprint and faster startup; robustness enhancements for profiling metrics by aligning CPU sample scaling with established implementations and strengthening GPU metrics validation; and expanded user onboarding with comprehensive documentation and standalone build capabilities for rocprofiler-systems examples. These changes improve maintainability, reduce risk in production deployments, and provide clearer performance insights across the ROCm profiling stack.
February 2026 – ROCm/rocm-systems: Focused on improving profiling usability, reliability, and trace accuracy. Implemented ROCprof-sys Profiling Tool Enhancements with custom presets and MPI-aware trace merging; fixed Perfetto UI correlation_id handling to prevent incorrect flow lines; improved multi-rank merged trace generation for cached data; delivered user-friendly validation, post-execution guidance, and visualization URLs; demonstrated strong collaboration with MPI tracing and Perfetto integration, enabling faster profiling setup, more accurate trace visualization, and better decision-making based on profiling data.
February 2026 – ROCm/rocm-systems: Focused on improving profiling usability, reliability, and trace accuracy. Implemented ROCprof-sys Profiling Tool Enhancements with custom presets and MPI-aware trace merging; fixed Perfetto UI correlation_id handling to prevent incorrect flow lines; improved multi-rank merged trace generation for cached data; delivered user-friendly validation, post-execution guidance, and visualization URLs; demonstrated strong collaboration with MPI tracing and Perfetto integration, enabling faster profiling setup, more accurate trace visualization, and better decision-making based on profiling data.
January 2026 monthly summary for ROCm/rocm-systems focusing on profiler enhancements and visualization consistency to improve reliability and developer experience.
January 2026 monthly summary for ROCm/rocm-systems focusing on profiler enhancements and visualization consistency to improve reliability and developer experience.
December 2025 performance-focused monthly summary for ROCm/rocm-systems highlighting feature delivery, bug fixes, and technical impact. Delivered unified Perfetto tracing enhancements with memory- and cache-aware optimizations, improving end-to-end trace reliability and diagnostics. Implemented centralized trace processing via a new Perfetto post-processing path, aligned default tracing with cached data, and reduced operational overhead. Also fixed a kernel_dispatch tracing bug affecting device identification. These changes reduce tracing overhead, accelerate root-cause analysis, and simplify maintenance of the tracing stack.
December 2025 performance-focused monthly summary for ROCm/rocm-systems highlighting feature delivery, bug fixes, and technical impact. Delivered unified Perfetto tracing enhancements with memory- and cache-aware optimizations, improving end-to-end trace reliability and diagnostics. Implemented centralized trace processing via a new Perfetto post-processing path, aligned default tracing with cached data, and reduced operational overhead. Also fixed a kernel_dispatch tracing bug affecting device identification. These changes reduce tracing overhead, accelerate root-cause analysis, and simplify maintenance of the tracing stack.
November 2025: Key features delivered, reliability improvements, and richer telemetry for ROCm/rocm-systems. Focused on observability, stable CPU sampling, and expanded agent data capture to support faster debugging and data-driven decisions.
November 2025: Key features delivered, reliability improvements, and richer telemetry for ROCm/rocm-systems. Focused on observability, stable CPU sampling, and expanded agent data capture to support faster debugging and data-driven decisions.
October 2025 monthly summary for ROCm/rocm-systems focusing on performance profiling improvements. Delivered ROCProfiler enhancements with PMC data integration, improved missing counter events handling, and corrected rocpd sampling logic to ensure accurate kernel identification; these changes increase the reliability of performance metrics and accelerate optimization efforts across GPU workloads.
October 2025 monthly summary for ROCm/rocm-systems focusing on performance profiling improvements. Delivered ROCProfiler enhancements with PMC data integration, improved missing counter events handling, and corrected rocpd sampling logic to ensure accurate kernel identification; these changes increase the reliability of performance metrics and accelerate optimization efforts across GPU workloads.

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