A. In the present paper, an efficient General Purpose Graphical Processing Unit (GPGPU)-based implementation of sparse grid Particle-In-Cell (PIC) methods is proposed. The parallelization, implementing novel strategies specific to Sparse-PIC methods and tailored to GPU architectures, provides speed-ups* as large as 100 on a single Tesla V100 GPU, with respect to sequential Computing Processing unit (CPU) execution; and a four order of magnitude reduction of the computational time in comparison with a standard PIC sequential CPU simulation. In addition, the simple implementation of the parallelization with the OpenACC framework offers portability to a large class of accelerators.