optimize-for-gpu

To accelerate CPU-bound Python workloads on an NVIDIA GPU, you replace pandas and NumPy operations with cuDF and CuPy. This transforms data processing into GPU-accelerated pipelines, minimizing host-to-device transfers to reduce runtime.

Yes, GPU acceleration works with scikit-learn workflows by using the cuML library. It mirrors the scikit-learn API to execute machine learning algorithms directly on NVIDIA GPUs, reducing compute time for large datasets.

The best way to run NetworkX-style graph analytics on a GPU is using the cuGraph library. It provides GPU-accelerated graph processing capabilities that speed up large-scale network analytics compared to CPU execution.

Yes, you can write custom CUDA kernels in Python using Numba CUDA or Warp. Numba CUDA allows hand-written kernels for array math, while Warp enables differentiable execution for simulation and spatial computing.

GPU acceleration pipelines run slower than expected when frequent host-to-device data transfers occur. You must keep data on the GPU across operations, applying memory-management best practices to reduce kernel-launch overhead and transfer latency.