ScalingIntelligence/KernelBench
Jun 2025 – Jul 2025
2 Months active
Languages Used
PythonJinja
Technical Skills
BenchmarkingDeep LearningDeep Learning FrameworksMachine LearningModel ImplementationNeural Network Design
Natalia
PROFILE
Natalia
Natalia Kokoromyti enhanced the ScalingIntelligence/KernelBench repository by refining its deep learning benchmark suite, focusing on code cleanup, maintainability, and workload coverage. Over two months, she implemented and restored a wide range of neural network layers, normalization techniques, and model architectures, including RNN, LSTM, and Transformer variants, using Python and PyTorch. Her work involved rigorous code refactoring, removal of deprecated modules, and integration of METR-driven quality gates to ensure reproducibility and accuracy. By addressing both feature expansion and bug fixes, Natalia improved the platform’s benchmarking signal quality and governance, demonstrating depth in deep learning frameworks and kernel benchmarking.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
110Total
Bugs
13
Commits
110
Features
23
Lines of code
22,649
Activity Months2
Your Network
11 people
Work History
July 2025
107 Commits • 21 Features
Jul 1, 2025July 2025 performance summary for ScalingIntelligence/KernelBench focused on delivering a lean, high-stability kernel platform with reduced technical debt and improved maintainability, while preserving/ restoring core functionality and aligning with METR-driven quality gates.
107 Commits • 21 Features
Jul 1, 2025July 2025 performance summary for ScalingIntelligence/KernelBench focused on delivering a lean, high-stability kernel platform with reduced technical debt and improved maintainability, while preserving/ restoring core functionality and aligning with METR-driven quality gates.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 performance highlights for ScalingIntelligence/KernelBench. Focused on cleaning up and hardening the Benchmark Suite to improve signal quality, expand DL workload coverage, and strengthen governance around benchmark tasks. Key refactors and feature additions were implemented to enhance accuracy, maintainability, and business value of KernelBench across real-world DL workloads.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 performance highlights for ScalingIntelligence/KernelBench. Focused on cleaning up and hardening the Benchmark Suite to improve signal quality, expand DL workload coverage, and strengthen governance around benchmark tasks. Key refactors and feature additions were implemented to enhance accuracy, maintainability, and business value of KernelBench across real-world DL workloads.
Activity
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Quality Metrics
Correctness95.8%
Maintainability96.0%
Architecture95.4%
Performance91.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaPython
Technical Skills
3D ConvolutionBenchmarkingCode CleanupCode OrganizationCode RefactoringComputer VisionConvolutional Neural NetworksDeep LearningDeep Learning FrameworksFile ManagementKernel BenchmarkingLSTMLinear AlgebraMachine LearningModel Definition
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