ROCm/rocMLIR
Jan 2025 – Sep 2025
9 Months active
Languages Used
C++CMakeMLIRPythonTOMLDockerfileShellLLVM IR
Technical Skills
Algorithm OptimizationBuild SystemsC++ DevelopmentCode GenerationCode RefactoringCompiler Development
Djordje Ramic
PROFILE
Djordje Ramic
Djoramic worked on the ROCm/rocMLIR repository, delivering features and fixes that advanced performance tuning, profiling, and CI infrastructure for GPU-accelerated machine learning workflows. Over nine months, Djoramic automated quick-tuning configuration generation using Python and C++, expanded mixed-precision support with bf16 and FP8 data types, and stabilized Docker-based build environments. Their work included refactoring file path handling for profiling outputs, improving device initialization logic, and upgrading CI/CD pipelines to support ROCm 7.0. By integrating benchmarking, low-level optimization, and compiler development skills, Djoramic improved reliability, maintainability, and scalability across ROCm/rocMLIR’s performance analysis and testing infrastructure.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
21Total
Bugs
5
Commits
21
Features
10
Lines of code
2,427
Activity Months9
Your Network
1303 people
Work History
September 2025
7 Commits • 3 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for ROCm/rocMLIR focused on stability, performance tooling, and ROCm 7.0 readiness. Delivered key MLIR/ROCm ecosystem improvements, upgraded CI/CD to align with ROCm 7.0, and enhanced profiling data for performance analysis while fixing a critical MIGraphX dialect registration issue.
7 Commits • 3 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for ROCm/rocMLIR focused on stability, performance tooling, and ROCm 7.0 readiness. Delivered key MLIR/ROCm ecosystem improvements, upgraded CI/CD to align with ROCm 7.0, and enhanced profiling data for performance analysis while fixing a critical MIGraphX dialect registration issue.
September 2025
August 2025
1 Commits
Aug 1, 2025In August 2025, delivered a critical bug fix for ROCm/rocMLIR that stabilizes device initialization and enhances robustness of the default device selection. Implemented a constructor-based approach in rocmlir-gen to set the default device, addressing initialization issues and reducing startup failures. The change improves reliability for users relying on automatic device selection, supports smoother onboarding, and reduces downstream troubleshooting.
August 2025
1 Commits
Aug 1, 2025In August 2025, delivered a critical bug fix for ROCm/rocMLIR that stabilizes device initialization and enhances robustness of the default device selection. Implemented a constructor-based approach in rocmlir-gen to set the default device, addressing initialization issues and reducing startup failures. The change improves reliability for users relying on automatic device selection, supports smoother onboarding, and reduces downstream troubleshooting.
July 2025
1 Commits
Jul 1, 2025July 2025 - ROCm/rocMLIR: Stabilized Rock dialect multi-buffer tests for gfx950 by adding an architecture-aware configuration and a new test file. Fixed a flaky multi-buffer test (commit 05daab4b7a0973e530f67cb4a208118aac312810), improving reliability of Rock dialect integration tests on gfx950. Business impact: reduces CI flakiness, improves gfx950 validation, and supports broader Rock dialect stability for release readiness. Technologies demonstrated: C++, LLVM/MLIR test infra, architecture-targeted testing, and CI automation.
1 Commits
Jul 1, 2025July 2025 - ROCm/rocMLIR: Stabilized Rock dialect multi-buffer tests for gfx950 by adding an architecture-aware configuration and a new test file. Fixed a flaky multi-buffer test (commit 05daab4b7a0973e530f67cb4a208118aac312810), improving reliability of Rock dialect integration tests on gfx950. Business impact: reduces CI flakiness, improves gfx950 validation, and supports broader Rock dialect stability for release readiness. Technologies demonstrated: C++, LLVM/MLIR test infra, architecture-targeted testing, and CI automation.
July 2025
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary for ROCm/rocMLIR focused on stabilizing the ROC Profiler path handling across chip architectures. Delivered a targeted fix to the rocprofv3 output path generation by refactoring file naming conventions to correctly handle different architectures, ensuring profiling results are saved to the appropriate locations and improving reliability of performance analysis.
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary for ROCm/rocMLIR focused on stabilizing the ROC Profiler path handling across chip architectures. Delivered a targeted fix to the rocprofv3 output path generation by refactoring file naming conventions to correctly handle different architectures, ensuring profiling results are saved to the appropriate locations and improving reliability of performance analysis.
May 2025
2 Commits • 2 Features
May 1, 2025May 2025 ROCm/rocMLIR: Delivered profiling and CI improvements that enhance measurement accuracy, reliability, and CI scalability. The changes support faster feedback cycles and more data-driven decisions for performance optimizations and ROCm version management.
2 Commits • 2 Features
May 1, 2025May 2025 ROCm/rocMLIR: Delivered profiling and CI improvements that enhance measurement accuracy, reliability, and CI scalability. The changes support faster feedback cycles and more data-driven decisions for performance optimizations and ROCm version management.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 performance summary for ROCm/rocMLIR focused on stabilizing Docker-based build environments for rocprofv3 and improving image reliability. Delivered targeted build optimizations and a version extraction fix to enhance reproducibility, CI stability, and developer onboarding.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 performance summary for ROCm/rocMLIR focused on stabilizing Docker-based build environments for rocprofv3 and improving image reliability. Delivered targeted build optimizations and a version extraction fix to enhance reproducibility, CI stability, and developer onboarding.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ROCm/rocMLIR focusing on FP8 performance path and GPU compatibility checks to improve benchmarking robustness and FP8 readiness.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ROCm/rocMLIR focusing on FP8 performance path and GPU compatibility checks to improve benchmarking robustness and FP8 readiness.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for ROCm/rocMLIR: Implemented performance tuning enhancements by adding FP8 and bf16 data type support, enabling broader mixed-precision benchmarking and optimization. This work was delivered through two commits: Add Fp8 to quick-tuning (#1753) and Add bf16 to tuning runner (#1739). Impact includes expanded precision options for benchmarking, enabling more effective tuning workflows and potential performance gains across workloads. Technologies demonstrated include C++/Python-based tuning components, integration with quick-tuning and tuning runner, and CI-ready changes in ROCm/rocMLIR.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for ROCm/rocMLIR: Implemented performance tuning enhancements by adding FP8 and bf16 data type support, enabling broader mixed-precision benchmarking and optimization. This work was delivered through two commits: Add Fp8 to quick-tuning (#1753) and Add bf16 to tuning runner (#1739). Impact includes expanded precision options for benchmarking, enabling more effective tuning workflows and potential performance gains across workloads. Technologies demonstrated include C++/Python-based tuning components, integration with quick-tuning and tuning runner, and CI-ready changes in ROCm/rocMLIR.
January 2025
3 Commits • 2 Features
Jan 1, 2025Month: 2025-01 | Repository: ROCm/rocMLIR. This period delivered automated quick-tuning configuration generation with enhanced selection and bf16 support for attention in the Rock dialect. Notable commits include 273d49b0e821c0f440b1de433c713c7fdf6683b2, 503893eefcb4cf59d466331358991312fef9ca83, and 112f8f46b38e4356cea1e44c6be373dbd8804a6d, which underpin automation, maintainability, and broader test coverage. Key achievements (top 3-5): - Automated quick-tuning configuration generator and enhanced selection: Adds a Python script to generate quick-tuning performance configurations, refactors C++ to consume generated .inc files, and includes logic to select optimal configurations based on performance data. Also sorts selected configurations by problem coverage to improve coverage and efficiency. - bf16 support for attention in Rock dialect with tuning updates: Introduces bf16 data type support in the Rock attention operation, updates operand/result type definitions, tuning to recognize bf16 elements, and adds bf16 test configurations for validation. - Data-driven optimization improvements: Implemented sorting of quick-tuning perfconfigs by problem coverage to prioritize configurations that cover more scenarios and improve overall performance gains. Major bugs fixed: No explicit major bugs reported this month. Stabilized quick-tuning generation path and bf16 attention tests to ensure reliable behavior across configurations and workloads. Overall impact and accomplishments: This work accelerates performance optimization cycles by automating configuration generation, improving selection quality via coverage-based ranking, and expanding bf16 support for mixed-precision workloads. The changes enhance maintainability, test coverage, and the ability to scale tuning across diverse workloads within ROCm/rocMLIR. Technologies/skills demonstrated: Python scripting for automation, C++ refactoring to integrate generated configuration files, performance data-driven tuning, type system updates for bf16, test configuration expansion, and data-driven decision making for configuration selection.
3 Commits • 2 Features
Jan 1, 2025Month: 2025-01 | Repository: ROCm/rocMLIR. This period delivered automated quick-tuning configuration generation with enhanced selection and bf16 support for attention in the Rock dialect. Notable commits include 273d49b0e821c0f440b1de433c713c7fdf6683b2, 503893eefcb4cf59d466331358991312fef9ca83, and 112f8f46b38e4356cea1e44c6be373dbd8804a6d, which underpin automation, maintainability, and broader test coverage. Key achievements (top 3-5): - Automated quick-tuning configuration generator and enhanced selection: Adds a Python script to generate quick-tuning performance configurations, refactors C++ to consume generated .inc files, and includes logic to select optimal configurations based on performance data. Also sorts selected configurations by problem coverage to improve coverage and efficiency. - bf16 support for attention in Rock dialect with tuning updates: Introduces bf16 data type support in the Rock attention operation, updates operand/result type definitions, tuning to recognize bf16 elements, and adds bf16 test configurations for validation. - Data-driven optimization improvements: Implemented sorting of quick-tuning perfconfigs by problem coverage to prioritize configurations that cover more scenarios and improve overall performance gains. Major bugs fixed: No explicit major bugs reported this month. Stabilized quick-tuning generation path and bf16 attention tests to ensure reliable behavior across configurations and workloads. Overall impact and accomplishments: This work accelerates performance optimization cycles by automating configuration generation, improving selection quality via coverage-based ranking, and expanding bf16 support for mixed-precision workloads. The changes enhance maintainability, test coverage, and the ability to scale tuning across diverse workloads within ROCm/rocMLIR. Technologies/skills demonstrated: Python scripting for automation, C++ refactoring to integrate generated configuration files, performance data-driven tuning, type system updates for bf16, test configuration expansion, and data-driven decision making for configuration selection.
January 2025
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Quality Metrics
Correctness88.6%
Maintainability88.6%
Architecture85.2%
Performance81.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeDockerfileGroovyLLVM IRMLIRPythonShellTOML
Technical Skills
Algorithm OptimizationBenchmarkingBuild SystemBuild System ConfigurationBuild SystemsC++C++ DevelopmentCI/CDCode GenerationCode RefactoringCompiler DevelopmentConfiguration ManagementData AnalysisDevOpsDocker
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