nod-ai/SHARK-Platform
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++CMake
Technical Skills
C++CMakeCompiler DevelopmentDeep LearningGPU ComputingMLIR
Alexandra Sidorova
PROFILE
Alexandra Sidorova
Alexandra Sidorova enhanced the nod-ai/SHARK-Platform by developing arithmetic pointwise operations and grouped convolutions within the Fusilli component. She updated the MLIR emission pathway and restructured the PointwiseAttr::Mode enum to support new operations, using C++ and CMake for implementation. Her work included expanding the benchmark driver and assembly emitter, as well as splitting pointwise tests into unary and binary categories to improve test maintainability. By integrating robust sample and lit test coverage, Alexandra ensured reliability and correctness. These contributions broadened the platform’s neural network expressiveness and performance, reflecting a deep understanding of compiler development and GPU computing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,969
Activity Months1
Your Network
1323 people
Work History
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 release focused on expanding Fusilli capabilities in nod-ai/SHARK-Platform: delivered arithmetic pointwise operations and grouped convolutions support, with updates to MLIR emission, the PointwiseAttr::Mode enum, benchmark driver, and assembly emitter. Added robust test coverage (samples and lit) and restructured tests by splitting pointwise tests into unary and binary categories to improve maintainability. No major bugs reported; all changes validated through expanded test suites and benchmarks. Business impact: broader model expressiveness, potential performance gains, and stronger reliability for advanced op combinations. Technologies demonstrated include Fusilli internals, MLIR emission, test infrastructure (lit tests), and benchmark integration.
3 Commits • 2 Features
Oct 1, 2025October 2025 release focused on expanding Fusilli capabilities in nod-ai/SHARK-Platform: delivered arithmetic pointwise operations and grouped convolutions support, with updates to MLIR emission, the PointwiseAttr::Mode enum, benchmark driver, and assembly emitter. Added robust test coverage (samples and lit) and restructured tests by splitting pointwise tests into unary and binary categories to improve maintainability. No major bugs reported; all changes validated through expanded test suites and benchmarks. Business impact: broader model expressiveness, potential performance gains, and stronger reliability for advanced op combinations. Technologies demonstrated include Fusilli internals, MLIR emission, test infrastructure (lit tests), and benchmark integration.
October 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture86.6%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMake
Technical Skills
C++CMakeCompiler DevelopmentDeep LearningGPU ComputingMLIRPerformance OptimizationSoftware RefactoringTesting
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