ROCm/rocm-systems
Feb 2026 – Mar 2026
2 Months active
Languages Used
C++CMakeRST
Technical Skills
AI IntegrationC++ developmentCMakeSystem Configurationnetwork programmingperformance profiling
ajanicijamd
PROFILE
Ajanicijamd
Worked on the ROCm/rocm-systems repository to deliver AI NIC profiling and configurable network performance testing features. Developed C++ and CMake-based enhancements that integrated AI NIC metric sampling into the profiling stack, enabling default collection of packets and bytes via AMD-SMI for improved observability of AI workloads. Updated system configuration and sampling logic to streamline adoption and support data-driven performance tuning. In a subsequent release, introduced environment-variable-based configurability for PAPI network refresh latency, allowing users to tailor network performance tests to diverse conditions. Focused on robust feature delivery, test reliability, and clear documentation, with no major bugs reported during this period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,362
Activity Months2
Your Network
2239 peopleSame Organization
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Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for ROCm/rocm-systems focusing on configurable network performance testing improvements. Key feature delivered: configurable net refresh latency for PAPI via an environment variable to allow users to adjust latency, improving flexibility and reliability of network performance tests. No major bug fixes reported for this repository this month; primary accomplishments center on feature delivery, test reliability, and clear documentation. Technologies demonstrated include environment-variable configurability, PAPI integration, Linux-based testing, and documentation alignment with testing plans and JIRA references.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for ROCm/rocm-systems focusing on configurable network performance testing improvements. Key feature delivered: configurable net refresh latency for PAPI via an environment variable to allow users to adjust latency, improving flexibility and reliability of network performance tests. No major bug fixes reported for this repository this month; primary accomplishments center on feature delivery, test reliability, and clear documentation. Technologies demonstrated include environment-variable configurability, PAPI integration, Linux-based testing, and documentation alignment with testing plans and JIRA references.
March 2026
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 focused on delivering AI NIC profiling capabilities within ROCm's profiling stack. Key outcomes include enabling AMD-SMI based AI NIC metric sampling in rocprofiler-systems via the ROCPROFSYS_USE_AINIC build flag, integrating NIC metrics (packets and bytes) into the existing profiling framework, and applying configuration and sampling logic changes to support AI NIC data. AI NIC support was made default-enabled to reduce setup friction and accelerate adoption. These enhancements improve observability for AI workloads, enabling data-driven performance tuning and earlier bottleneck detection. There were no major bugs reported; the month centered on feature delivery, stability, and readiness for broader usage.
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 focused on delivering AI NIC profiling capabilities within ROCm's profiling stack. Key outcomes include enabling AMD-SMI based AI NIC metric sampling in rocprofiler-systems via the ROCPROFSYS_USE_AINIC build flag, integrating NIC metrics (packets and bytes) into the existing profiling framework, and applying configuration and sampling logic changes to support AI NIC data. AI NIC support was made default-enabled to reduce setup friction and accelerate adoption. These enhancements improve observability for AI workloads, enabling data-driven performance tuning and earlier bottleneck detection. There were no major bugs reported; the month centered on feature delivery, stability, and readiness for broader usage.
Activity
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture93.4%
Performance86.6%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++CMakeRST
Technical Skills
AI IntegrationC++ developmentCMakeSystem Configurationnetwork programmingperformance profilingperformance testingsystem programming
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