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There are several ways to use CUDA for a million-length array:

1. CUDA C/C++: Writing CUDA code using the CUDA C/C++ programming language. This involves defining kernels, uploading data to the GPU, executing the kernels, and downloading the results back to the CPU.

2. CUDA Python: Using CUDA in a Python environment with libraries like PyCUDA or Numba. This allows for easy integration of CUDA code into existing Python code.

3. CUDA Fortran: Writing CUDA code using Fortran with the CUDA Fortran compiler. This is useful for scientific computing applications.

4. CUDA Libraries: Using pre-built CUDA libraries like cuBLAS, cuFFT, and cuDNN that are optimized for certain tasks like matrix multiplication, FFT, and deep learning.

5. CUDA-aware MPI: Utilizing CUDA-aware MPI libraries like MVAPICH2-GDR, OpenMPI-GDR, and Intel MPI to enable inter-node GPU communication for distributed computing.

6. CUDA-accelerated algorithms in other software: Many software packages like MATLAB, Mathematica, and R have CUDA-accelerated algorithms built-in, allowing for rapid computation on GPUs without needing to write CUDA code.