NVIDIA/warp
Apr 2026 – Apr 2026
1 Month active
Languages Used
Python
Technical Skills
CUDAJAXMachine LearningNumerical Computing
Suhyeon Kim
PROFILE
Suhyeon Kim
Worked on the NVIDIA/warp repository to implement launch_dims support for autodiff in the jax_kernel, enabling users to specify launch dimensions that drive both forward and adjoint gradient computations when backward mode is active. This involved capturing launch_dims in the kernel resolution path and updating the FFI_DIFF_KERNEL_REGISTRY cache to ensure wrappers with different launch_dims remain isolated, reducing the risk of cache-induced regressions. Comprehensive tests were added to validate launch_dims acceptance, gradient correctness for high-dimensional inputs, and wrapper independence. The work demonstrated proficiency in Python, CUDA, and JAX, with a focus on robust kernel interfacing and test-driven development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
355
Activity Months1
Your Network
63 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 performance summary for NVIDIA/warp: Implemented launch_dims support for autodiff in jax_kernel, enabling user-specified launch_dims to drive both forward and adjoint gradient computations when enable_backward is True. The change captures launch_dims in the _resolve_launch_dims path and updates the FFI_DIFF_KERNEL_REGISTRY cache so wrappers with different launch_dims are isolated, preventing cache-induced regressions. Added comprehensive tests covering acceptance of launch_dims, gradient correctness for a 4-D scalar-dtype input with a 3-D tid, and independent wrappers for different launch_dims. Commit reference: 6e5730d0ab01cd4044332ccd7af9584192cf3824. Impact: improved gradient accuracy and user control for high-dimensional kernels, reduced risk of cache-related regressions, and strengthened CI coverage. Technologies demonstrated: Python, JAX autodiff integration, CUDA/JAX kernel interfacing, FFI, robust caching, and test-driven development.
1 Commits • 1 Features
Apr 1, 2026April 2026 performance summary for NVIDIA/warp: Implemented launch_dims support for autodiff in jax_kernel, enabling user-specified launch_dims to drive both forward and adjoint gradient computations when enable_backward is True. The change captures launch_dims in the _resolve_launch_dims path and updates the FFI_DIFF_KERNEL_REGISTRY cache so wrappers with different launch_dims are isolated, preventing cache-induced regressions. Added comprehensive tests covering acceptance of launch_dims, gradient correctness for a 4-D scalar-dtype input with a 3-D tid, and independent wrappers for different launch_dims. Commit reference: 6e5730d0ab01cd4044332ccd7af9584192cf3824. Impact: improved gradient accuracy and user control for high-dimensional kernels, reduced risk of cache-related regressions, and strengthened CI coverage. Technologies demonstrated: Python, JAX autodiff integration, CUDA/JAX kernel interfacing, FFI, robust caching, and test-driven development.
April 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
CUDAJAXMachine LearningNumerical Computing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline