GPU Support

Most queries and learning APIs support both CPU and GPU implementations. To use the GPU implementations you need to move the circuit and the dataset to GPU, then call the corresponding API.

Moving to GPU

Moving Data to GPU

Currently, the APIs support CuArray type of gpu implemetations. One simple way to move to gpu is using the cu function from CUDA.jl.

using CUDA
train_x_gpu, test_x_gpu = cu.(train_x, test_x)

In case of missing values we use Missing type, so for example if you have categorical features with some missing values, the data type on gpu would be CuArray{Union{Missing, UInt32}}.

Moving ProbCircuits to GPU

ProbCircuits are stored in DAG structure and are not GPU friendly by default. So, we convert them into BitsProbCircuits (or bit circuits) as a lower level representation that is GPU friendly. The GPU version of bit circuits has type CuBitsProbCircuit, so to move your circuit to GPU you can simply do:

bpc = CuBitsProbCircuit(circuit);


The GPU supported APIs generally have the same name as their CPU counterpart, for a comprehensive list of supported functions see the API documentation. For example, we support the following on gpu: