Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simulating Execution Time of Tensor Programs using Graph Neural Networks   Optimizing the execution time of tensor program, e.g., a convolution,
involves finding its optimal configuration. Searching the configuration space
exhaustively is typically infeasible in practice. In line with recent research
using TVM, we propose to learn a surrogate model to overcome this issue. The
model is trained on an acyclic graph called an abstract syntax tree, and
utilizes a graph convolutional network to exploit structure in the graph. We
claim that a learnable graph-based data processing is a strong competitor to
heuristic-based feature extraction. We present a new dataset of graphs
corresponding to configurations and their execution time for various tensor
programs. We provide baselines for a runtime prediction task.

20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con