  Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the
bottleneck kernel of sparse tensor decomposition. In this work, we propose a
GPU-based algorithm design to address the key challenges in accelerating
spMTTKRP computation, including (1) eliminating global atomic operations across
GPU thread blocks, (2) avoiding the intermediate values being communicated
between GPU thread blocks and GPU global memory, and (3) ensuring a balanced
distribution of workloads across GPU thread blocks. Our approach also supports
dynamic tensor remapping, enabling the above optimizations in all the modes of
the input tensor. Our approach achieves a geometric mean speedup of 1.5x, 2.0x,
and 21.7x in total execution time across widely used datasets compared with the
state-of-the-art GPU implementations. Our work is the only GPU implementation
that can support tensors with modes greater than 4 since the state-of-the-art
works have implementation constraints for tensors with a large number of modes.
