
Pranjal Swarup contributed to the ROCm/rocprofiler-systems repository by developing and enhancing profiling and monitoring features for AMD GPUs. Over six months, Pranjal delivered tools such as a multiprocess proto file merger and improved metrics visibility, streamlining data handling and diagnostics. He addressed hardware interaction challenges by refining GPU power and temperature data retrieval and maintained compatibility with evolving dependencies through targeted updates. His work involved C++, CMake, and Docker, emphasizing build system integration, CI/CD reliability, and environment management. Pranjal’s engineering demonstrated depth in system programming and configuration management, resulting in more robust, maintainable, and reproducible profiling workflows.

June 2025 performance summary for ROCm/rocprofiler-systems focused on reliability, reproducibility, and test stability across CI, Docker environments, and example workloads. Key improvements include explicit dependency builds for Dyninst, migration to Miniforge-based Python environments in Dockerfiles, and enhanced RCCL testing infrastructure in examples with improved test compatibility.
June 2025 performance summary for ROCm/rocprofiler-systems focused on reliability, reproducibility, and test stability across CI, Docker environments, and example workloads. Key improvements include explicit dependency builds for Dyninst, migration to Miniforge-based Python environments in Dockerfiles, and enhanced RCCL testing infrastructure in examples with improved test compatibility.
May 2025 monthly summary for ROCm/rocprofiler-systems with a focus on observability and configurability improvements. Key delivered feature: ROCPROFSYS AMD SMI Metrics Visibility Enhancement, enabling visibility of the amd-smi metrics property when using rocprof-sys-avail by removing the 'advanced' category from ROCPROFSYS_AMD_SMI_METRICS and enabling configurable metrics through the system availability tool. This work was committed as 4c7560c78c7741725c8ef247df5a6569a2d79f98 (ROCPROFSYS_AMD_SMI_METRICS visibility (#208)). Impact: enhances monitoring, diagnostics, and tuning capabilities for AMD GPUs within ROCprofiler-systems, reducing manual steps and accelerating performance analysis. Technologies/skills demonstrated: ROCm ROCprofiler-systems, AMD SMI metrics, system availability tooling, metrics visibility/configuration, git-based patch delivery and review.
May 2025 monthly summary for ROCm/rocprofiler-systems with a focus on observability and configurability improvements. Key delivered feature: ROCPROFSYS AMD SMI Metrics Visibility Enhancement, enabling visibility of the amd-smi metrics property when using rocprof-sys-avail by removing the 'advanced' category from ROCPROFSYS_AMD_SMI_METRICS and enabling configurable metrics through the system availability tool. This work was committed as 4c7560c78c7741725c8ef247df5a6569a2d79f98 (ROCPROFSYS_AMD_SMI_METRICS visibility (#208)). Impact: enhances monitoring, diagnostics, and tuning capabilities for AMD GPUs within ROCprofiler-systems, reducing manual steps and accelerating performance analysis. Technologies/skills demonstrated: ROCm ROCprofiler-systems, AMD SMI metrics, system availability tooling, metrics visibility/configuration, git-based patch delivery and review.
March 2025 (2025-03) – ROCm/rocprofiler-systems focused maintenance to preserve compatibility with tooling dependencies and ensure continuous profiling capability. Key work consisted of a critical BinUtil compatibility update for profiling tools, safeguarding data capture accuracy and reliability across the ROCm profiler stack.
March 2025 (2025-03) – ROCm/rocprofiler-systems focused maintenance to preserve compatibility with tooling dependencies and ensure continuous profiling capability. Key work consisted of a critical BinUtil compatibility update for profiling tools, safeguarding data capture accuracy and reliability across the ROCm profiler stack.
January 2025: Delivered targeted RHEL Docker image enhancements for ROCm/rocprofiler-systems to improve MPI testing and dependencies, and fixed CI/test configuration issues to stabilize MPI workflows.
January 2025: Delivered targeted RHEL Docker image enhancements for ROCm/rocprofiler-systems to improve MPI testing and dependencies, and fixed CI/test configuration issues to stabilize MPI workflows.
December 2024 performance summary for ROCm/rocprofiler-systems. Delivered a Multiprocess Proto Files Merger and Integration feature that consolidates multiprocess proto outputs into a single file, integrated into build/execution, with script installation and environment exposure enabling rank 0 auto-merging. This streamlines data handling, reduces post-processing time, and improves data reliability. No major bug fixes were recorded this month. Business impact: faster, more reliable profiling data workflow and easier maintenance.
December 2024 performance summary for ROCm/rocprofiler-systems. Delivered a Multiprocess Proto Files Merger and Integration feature that consolidates multiprocess proto outputs into a single file, integrated into build/execution, with script installation and environment exposure enabling rank 0 auto-merging. This streamlines data handling, reduces post-processing time, and improves data reliability. No major bug fixes were recorded this month. Business impact: faster, more reliable profiling data workflow and easier maintenance.
November 2024 monthly summary for ROCm/rocprofiler-systems focusing on MI300A tracing improvements. The core deliverable this month was a fix to retrieve GPU power and temperature data for MI300A traces by using the appropriate RSMI functions and parameters, ensuring critical hardware statistics are accurately captured and available for analysis. The work improves data reliability across the tracing pipeline and supports more accurate performance insights.
November 2024 monthly summary for ROCm/rocprofiler-systems focusing on MI300A tracing improvements. The core deliverable this month was a fix to retrieve GPU power and temperature data for MI300A traces by using the appropriate RSMI functions and parameters, ensuring critical hardware statistics are accurately captured and available for analysis. The work improves data reliability across the tracing pipeline and supports more accurate performance insights.
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