
Mehdi Ataei developed automatic differentiation support for Warp kernels within the NVIDIA/warp repository, enabling seamless integration with JAX for machine learning and scientific computing workflows. He implemented adjoint computation in the jax_kernel module, allowing Warp kernels to be differentiable and thus suitable for gradient-based optimization. Using C++ and Python, Mehdi leveraged skills in CUDA, FFI, and kernel development to deliver this feature, along with comprehensive documentation and unit tests to ensure reliability and ease of adoption. His work addressed integration friction between Warp and JAX, laying a technical foundation for more flexible and efficient experimentation in differentiable programming.

October 2025 Monthly Summary for NVIDIA/warp: Delivered JAX Warp Kernel Automatic Differentiation feature with adjoint computation, plus docs and tests. This enables differentiable Warp kernels within JAX for ML and scientific computing workflows, reducing integration friction and accelerating experimentation.
October 2025 Monthly Summary for NVIDIA/warp: Delivered JAX Warp Kernel Automatic Differentiation feature with adjoint computation, plus docs and tests. This enables differentiable Warp kernels within JAX for ML and scientific computing workflows, reducing integration friction and accelerating experimentation.
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