GPU implementation using CuPy

The current implementation focuses on using the GPU for many internal wave function operations, so that

• Wave function parameters are stored on the GPU

  • This allows the calculations to be done on the GPU without copying the parameters over each time.
  • When assigning wf parameters, arrays need to be moved to GPU
  • During optimization, parameters serialize to CPU and deserialize back to GPU

• Wave function methods return values on the CPU

  • This allows functions like vmc and dmc to remain unchanged, and operate only on CPU.

The Slater determinant helper functions use the GPU:

• Orbitals object returns AOs and MOs on GPU.
• determinant_tools.compute_value takes in GPU arrays from the Slater object and outputs CPU arrays.

In addition, some calculations outside of wf evaluation are also carried out on GPU

• RDM: since the RDM accumulators evaluate orbitals directly, the orbital arrays and internal calculations are on GPU. All return values are on CPU.

  (Note: wf.testvalue_many returns an array on CPU. Since it is only used in RDMs, which move the arrays back to GPU anyway, it might make sense for testvalue_many to return GPU arrays instead.)

• Ewald: reciprocal_space_electron() uses GPU internally. Its inputs and outpus are CPU arrays.