Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Out-of-Core GPU Gradient Boosting   GPU-based algorithms have greatly accelerated many machine learning methods;
however, GPU memory is typically smaller than main memory, limiting the size of
training data. In this paper, we describe an out-of-core GPU gradient boosting
algorithm implemented in the XGBoost library. We show that much larger datasets
can fit on a given GPU, without degrading model accuracy or training time. To
the best of our knowledge, this is the first out-of-core GPU implementation of
gradient boosting. Similar approaches can be applied to other machine learning
algorithms

9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys