Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Characterizing and Understanding GCNs on GPU   Graph convolutional neural networks (GCNs) have achieved state-of-the-art
performance on graph-structured data analysis. Like traditional neural
networks, training and inference of GCNs are accelerated with GPUs. Therefore,
characterizing and understanding the execution pattern of GCNs on GPU is
important for both software and hardware optimization. Unfortunately, to the
best of our knowledge, there is no detailed characterization effort of GCN
workloads on GPU. In this paper, we characterize GCN workloads at inference
stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and
exploration, we propose several useful guidelines for both software
optimization and hardware optimization for the efficient execution of GCNs on
GPU.

12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
14
The categories of this paper are: math-ph