Xilinx/onnx-mlir
Nov 2024 – Mar 2025
4 Months active
Languages Used
C++MLIR
Technical Skills
Command-line InterfaceCompiler DevelopmentLow-Level OptimizationMLIRMLIR DialectsMemory Management
Haruki Imai
PROFILE
Haruki Imai
Imaihal contributed to the Xilinx/onnx-mlir repository by developing and optimizing compiler features focused on memory efficiency, parallelism, and ONNX graph simplification. Over four months, Imaihal engineered deferred constants stickification and artifact emission controls to reduce memory and disk usage during NNPA compilation, leveraging C++ and MLIR for low-level optimization. They introduced a threaded compilation option, enabling scalable parallel builds and faster CI cycles by configuring MLIR contexts for thread pools. Additionally, Imaihal parallelized ConstProp reductions with adaptive execution and rewrote ONNX graph patterns to improve performance. The work demonstrated depth in compiler development, parallel computing, and performance engineering.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
1,518
Activity Months4
Your Network
80 peopleWork History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Xilinx/onnx-mlir. Focus on delivering high-impact features, minimizing defects, and advancing performance and efficiency for ONNX workloads.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Xilinx/onnx-mlir. Focus on delivering high-impact features, minimizing defects, and advancing performance and efficiency for ONNX workloads.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary: Implemented ConstProp parallel reduction optimization with adaptive execution in Xilinx/onnx-mlir, accelerating the ConstProp compilation path by parallelizing reduction computations with parallelFor() and adding a small-tensor fallback to minimize overhead. Added a test file to validate correctness and performance. The change is tracked under commit ab75f99a3475ba5143bbd5906ab2825e2a83484e (Parallelization of ConstProp compilation (#3042)). This work did not introduce critical bugs; it reduces compile-time for larger models and improves scalability of the ConstProp phase. Skills demonstrated include parallel programming, MLIR/Compiler optimizations, test-driven development, and performance-focused engineering.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary: Implemented ConstProp parallel reduction optimization with adaptive execution in Xilinx/onnx-mlir, accelerating the ConstProp compilation path by parallelizing reduction computations with parallelFor() and adding a small-tensor fallback to minimize overhead. Added a test file to validate correctness and performance. The change is tracked under commit ab75f99a3475ba5143bbd5906ab2825e2a83484e (Parallelization of ConstProp compilation (#3042)). This work did not introduce critical bugs; it reduces compile-time for larger models and improves scalability of the ConstProp phase. Skills demonstrated include parallel programming, MLIR/Compiler optimizations, test-driven development, and performance-focused engineering.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for Xilinx/onnx-mlir: Delivered threaded compilation control (-j) to parallelize the build process, enabling scalable and faster compilation for large models. The -j option specifies the number of threads for the parallel compilation; when unset, all CPUs are used by default. The MLIR context is configured to use the chosen thread pool, driving improved compilation performance across CI and local development. Change tracked in commit 6d2b1d4cefe91d74aed1fd01a51dc11e0c0caed2, addressing performance and build-time efficiency. This supports faster validation cycles, reduces feedback time, and demonstrates strong proficiency in build tooling and MLIR integration.
1 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for Xilinx/onnx-mlir: Delivered threaded compilation control (-j) to parallelize the build process, enabling scalable and faster compilation for large models. The -j option specifies the number of threads for the parallel compilation; when unset, all CPUs are used by default. The MLIR context is configured to use the chosen thread pool, driving improved compilation performance across CI and local development. Change tracked in commit 6d2b1d4cefe91d74aed1fd01a51dc11e0c0caed2, addressing performance and build-time efficiency. This supports faster validation cycles, reduces feedback time, and demonstrates strong proficiency in build tooling and MLIR integration.
January 2025
November 2024
2 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for Xilinx/onnx-mlir: Delivered NNPA compilation memory efficiency improvements and artifact-management enhancements. Implemented deferring constants stickification to reduce peak memory and added a flag to emit only temporary artifacts, avoiding full MLIR emission to cut memory and disk pressure. Result: lower resource usage, faster iteration in CI, and improved stability for NNPA workflows. Technologies demonstrated include MLIR-based NNPA compilation, memory management techniques, and configurable build-time artifact handling.
November 2024
2 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for Xilinx/onnx-mlir: Delivered NNPA compilation memory efficiency improvements and artifact-management enhancements. Implemented deferring constants stickification to reduce peak memory and added a flag to emit only temporary artifacts, avoiding full MLIR emission to cut memory and disk pressure. Result: lower resource usage, faster iteration in CI, and improved stability for NNPA workflows. Technologies demonstrated include MLIR-based NNPA compilation, memory management techniques, and configurable build-time artifact handling.
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Quality Metrics
Correctness92.0%
Maintainability88.0%
Architecture88.0%
Performance92.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
Command-Line Interface DesignCommand-line InterfaceCompiler DevelopmentCompiler OptimizationLow-Level OptimizationMLIRMLIR DialectsMemory ManagementONNXParallel ComputingPerformance EngineeringPerformance Optimization
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