How to use GPU acceleration for computation in Torch?

To accelerate calculations using GPU in Torch, first, make sure your computer has a GPU that supports CUDA installed, along with the corresponding CUDA and cuDNN libraries. Then, transfer tensors to the GPU for computation in Torch.

Here are the steps to accelerate computations using a GPU in Torch.

1. Import the Torch and CUDA modules.

require 'torch'
require 'cutorch'

1. Move the tensor to GPU:

local tensor = torch.Tensor(3, 3):cuda()

1. Perform calculations on the GPU.

local a = torch.CudaTensor(3, 3):fill(1)
local b = torch.CudaTensor(3, 3):fill(2)
local c = a + b

In the above example, we first created two tensors, a and b, of size 3×3 and filled them with values of 1 and 2, respectively. We then added these two tensors to get tensor c, and this computation was performed on the GPU.

By doing so, you can leverage the parallel computing capabilities of the GPU to accelerate the computation process in Torch.