  By building on recent advances in the use of randomized trace estimation to
drastically reduce the memory footprint of adjoint-state methods, we present
and validate an imaging approach that can be executed exclusively on
accelerators. Results obtained on field-realistic synthetic datasets, which
include salt and anisotropy, show that our method produces high-fidelity
images. These findings open the enticing perspective of 3D wave-based inversion
technology with a memory footprint that matches the hardware and that runs
exclusively on clusters of GPUs without the undesirable need to offload certain
tasks to CPUs.
