  We propose SparsePipe, an efficient and asynchronous parallelism approach for
handling 3D point clouds with multi-GPU training. SparsePipe is built to
support 3D sparse data such as point clouds. It achieves this by adopting
generalized convolutions with sparse tensor representation to build expressive
high-dimensional convolutional neural networks. Compared to dense solutions,
the new models can efficiently process irregular point clouds without densely
sliding over the entire space, significantly reducing the memory requirements
and allowing higher resolutions of the underlying 3D volumes for better
performance.
  SparsePipe exploits intra-batch parallelism that partitions input data into
multiple processors and further improves the training throughput with
inter-batch pipelining to overlap communication and computing. Besides, it
suitably partitions the model when the GPUs are heterogeneous such that the
computing is load-balanced with reduced communication overhead.
  Using experimental results on an eight-GPU platform, we show that SparsePipe
can parallelize effectively and obtain better performance on current point
cloud benchmarks for both training and inference, compared to its dense
solutions.
