Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DistGNN: Scalable Distributed Training for Large-Scale Graph Neural
  Networks   Full-batch training on Graph Neural Networks (GNN) to learn the structure of
large graphs is a critical problem that needs to scale to hundreds of compute
nodes to be feasible. It is challenging due to large memory capacity and
bandwidth requirements on a single compute node and high communication volumes
across multiple nodes. In this paper, we present DistGNN that optimizes the
well-known Deep Graph Library (DGL) for full-batch training on CPU clusters via
an efficient shared memory implementation, communication reduction using a
minimum vertex-cut graph partitioning algorithm and communication avoidance
using a family of delayed-update algorithms. Our results on four common GNN
benchmark datasets: Reddit, OGB-Products, OGB-Papers and Proteins, show up to
3.7x speed-up using a single CPU socket and up to 97x speed-up using 128 CPU
sockets, respectively, over baseline DGL implementations running on a single
CPU socket

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The categories of this paper are: eess
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th