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....
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
26.04.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1904.11876 |