  This paper presents GPU performance optimization and scaling results for
inference models of the Sparse Deep Neural Network Challenge 2020. Demands for
network quality have increased rapidly, pushing the size and thus the memory
requirements of many neural networks beyond the capacity of available
accelerators. Sparse deep neural networks (SpDNN) have shown promise for
reining in the memory footprint of large neural networks. However, there is
room for improvement in implementing SpDNN operations on GPUs. This work
presents optimized sparse matrix multiplication kernels fused with the ReLU
function. The optimized kernels reuse input feature maps from the shared memory
and sparse weights from registers. For multi-GPU parallelism, our SpDNN
implementation duplicates weights and statically partition the feature maps
across GPUs. Results for the challenge benchmarks show that the proposed kernel
design and multi-GPU parallelization achieve up to 180 tera-edges per second
inference throughput. These results are up to 4.3x faster for a single GPU and
an order of magnitude faster at full scale than those of the champion of the
2019 Sparse Deep Neural Network Graph Challenge for the same generation of
NVIDIA V100 GPUs. Using the same implementation, we also show single-GPU
throughput on NVIDIA A100 is 2.37$\times$ faster than V100.

Distributed, Parallel, and Cluster Computing;Machine Learning