GPU Acceleration

CUDA.jl Version 3 Required

Bloqade currently does not work with the latest version (4.x) of the CUDA package. As a workaround you will need to install version 3. The installation instructions below should already address this.

Bloqade supports CUDA acceleration. To use CUDA acceleration, you will need a NVIDIA graphics processing unit (GPU).


To use CUDA accelerators, you need to install the CUDA package and the Adapt package:

pkg> add CUDA@3 Adapt

This will automatically download all the needed dependencies of the CUDA toolkit. This functionality requires CUDA toolkit 11.6+ and CUSPARSE 11.4+. You can check your version via

julia> CUDA.version()

Using CUDA

Converting your CPU-based simulation to CUDA-based simulation is extremely simple: just use the adapt function from Adapt on the register object, which will convert the CPU-based register to a CUDA-based register, e.g.:

using CUDA, Adapt
reg = zero_state(5)
dreg = adapt(CuArray, reg) # device register

For emulation, you can call adapt on your emulation object to convert everything (emulation intermediate memory, etc.) into the GPU memory, e.g.:

adapt(CuArray, KrylovEvolution(reg, clocks, h))

To perform operations after emulation (such as calculating the rydberg density) you will need to move the object out of GPU memory. This can be done so via adapt again:

# get the problem onto the GPU and perform emulation there:
gpu_prob = adapt(CuArray, SchrodingerProblem(register, time_span, hamiltonian))
integrator = init(gpu_prob, Vern9())

# get the problem off the GPU for subsequent processing/analysis:
prob = adapt(Array, gpu_prob)

Other code written with Bloqade should work with CUDA automatically.