ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations. (arXiv:2210.12415v4 [cs.LG] UPDATED)

Deep learning models rely on highly optimized tensor libraries for efficient
inference on heterogeneous hardware. Current deep compilers typically
predetermine layouts of tensors and then optimize loops of operators. However,
such unidirectional and one-off workflow strictly separates graph-level
optimization and operator-level optimization into different system layers,
missing opportunities for unified tuning. This paper proposes ALT, a compiler
that performs joint graph- and operator-level optimizations for deep models.
ALT provides a generic transformation module to manipulate layouts and loops
with easy-to-use …

arxiv boosting breaking deep learning deep learning performance graph performance