  With the unprecedented proliferation of machine learning software, there is
an ever-increasing need to generate efficient code for such applications.
State-of-the-art deep-learning compilers like TVM and Halide incorporate a
learning-based performance model to search the space of valid implementations
of a given deep learning algorithm. For a given application, the model
generates a performance metric such as the run time without executing the
application on hardware. Such models speed up the compilation process by
obviating the need to benchmark an enormous number of candidate
implementations, referred to as schedules, on hardware. Existing performance
models employ feed-forward networks, recurrent networks, or decision tree
ensembles to estimate the performance of different implementations of a neural
network. Graphs present a natural and intuitive way to model deep-learning
networks where each node represents a computational stage or operation.
Incorporating the inherent graph structure of these workloads in the
performance model can enable a better representation and learning of
inter-stage interactions. The accuracy of a performance model has direct
implications on the efficiency of the search strategy, making it a crucial
component of this class of deep-learning compilers. In this work, we develop a
novel performance model that adopts a graph representation. In our model, each
stage of computation represents a node characterized by features that capture
the operations performed by the stage. The interaction between nodes is
achieved using graph convolutions. Experimental evaluation shows a 7:75x and
12x reduction in prediction error compared to the Halide and TVM models,
respectively.
