intel/mlir-extensions
Dec 2025 – May 2026
4 Months active
Languages Used
C++MarkdownMLIR
Technical Skills
LLVMOpenCLSPIR-Vbackend developmentdocumentationkernel optimization
Md Abdullah Shahneous Bari
PROFILE
Md Abdullah Shahneous Bari
Worked on performance optimization and backend development for the llvm/llvm-project and intel/mlir-extensions repositories, focusing on GPU programming and compiler infrastructure. Delivered features such as vector-length support in the LLVM SPIR-V backend for OpenCL/MLIR, enabling large vector operations and improving workflow flexibility. Enhanced XeVM by integrating the libocloc API for native binary generation using C++ and CMake, reducing overhead and streamlining compilation. Implemented GEMM and Flash Attention performance optimizations, including robust testing frameworks and benchmarking on Intel GPUs. Contributed to documentation and profiling workflows, emphasizing correctness, reproducibility, and efficient memory management within MLIR and related GPU-accelerated machine learning pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
7
Lines of code
2,896
Activity Months4
Your Network
3103 peopleSame Organization
@intel.com2258
Work History
May 2026
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary highlighting key features delivered, major improvements, and business impact across llvm-project and MLIR extensions.
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary highlighting key features delivered, major improvements, and business impact across llvm-project and MLIR extensions.
May 2026
April 2026
4 Commits • 3 Features
Apr 1, 2026April 2026: Delivered key performance-focused enhancements across XeVM/MLIR and MLIR extensions. XeVM: migrated native binary generation to the libocloc API with a new CMake module, preferring in-process API and falling back to the CLI to reduce overhead. GEMM: implemented large GRF-based GEMM with bias, plus GPU memory management and optimized kernel execution for large datasets. Benchmarking: produced a 4K GEMM performance report for the BMG platform to guide optimizations and future work. Testing: updated GEMM tests for performance consistency and correctness (large GRF variants, barrier-usage adjustments). Impact: reduced binary generation overhead, improved GPU GEMM throughput and correctness, and established a solid performance evaluation baseline.
April 2026
4 Commits • 3 Features
Apr 1, 2026April 2026: Delivered key performance-focused enhancements across XeVM/MLIR and MLIR extensions. XeVM: migrated native binary generation to the libocloc API with a new CMake module, preferring in-process API and falling back to the CLI to reduce overhead. GEMM: implemented large GRF-based GEMM with bias, plus GPU memory management and optimized kernel execution for large datasets. Benchmarking: produced a 4K GEMM performance report for the BMG platform to guide optimizations and future work. Testing: updated GEMM tests for performance consistency and correctness (large GRF variants, barrier-usage adjustments). Impact: reduced binary generation overhead, improved GPU GEMM throughput and correctness, and established a solid performance evaluation baseline.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary focusing on key accomplishments in intel/mlir-extensions, with an emphasis on Kernel Profiling Documentation Enhancement to improve profiling workflow and reproducibility.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary focusing on key accomplishments in intel/mlir-extensions, with an emphasis on Kernel Profiling Documentation Enhancement to improve profiling workflow and reproducibility.
March 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary focusing on delivering vector-length support in the LLVM SPIR-V backend for OpenCL/MLIR, enabling larger vector operations and unblocking related internal workflows while maintaining a temporary patch approach.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary focusing on delivering vector-length support in the LLVM SPIR-V backend for OpenCL/MLIR, enabling larger vector operations and unblocking related internal workflows while maintaining a temporary patch approach.
Activity
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Quality Metrics
Correctness95.6%
Maintainability84.4%
Architecture90.0%
Performance93.4%
AI Usage28.8%
Skills & Technologies
Programming Languages
CC++CMakeMLIRMarkdown
Technical Skills
Build SystemsC++Compiler DevelopmentGPU ProgrammingGPU programmingLLVMLow-Level OptimizationMLIRMLIR DialectsMachine learningMatrix operationsOpenCLPerformance optimizationSPIR-VTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
llvm/llvm-project
Apr 2026 – May 2026
2 Months active
Languages Used
C++CMakeCMLIR
Technical Skills
Build SystemsC++Compiler DevelopmentMLIRXeVMGPU Programming