论文标题
跨度：3D点云的平行深度学习
SparsePipe: Parallel Deep Learning for 3D Point Clouds
论文作者
论文摘要
我们提出了稀疏管，这是一种通过多GPU训练来处理3D点云的高效和异步平行性方法。 SparsePipe旨在支持3D稀疏数据，例如点云。它通过采用稀疏张量表示的广义卷积来建立表达性高维卷积神经网络来实现这一目标。与密集的解决方案相比，新模型可以有效地处理不规则的点云，而无需在整个空间上密集滑动，从而大大降低了内存需求并允许基础3D卷的更高分辨率以获得更好的性能。 SparsePipe利用了批处理并行性，将数据分配到多个处理器中，并通过批处理间管道上的管道内进一步改善训练吞吐量，从而重叠通信和计算。此外，当GPU异质性时，它可以适当地对模型进行分区，以使计算与降低的通信开销相平衡。 使用八个GPU平台上的实验结果，我们表明，与其密集的溶液相比，跨度Pipe可以有效地平行并在训练和推理上获得更好的训练和推理的性能。
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.
