  To enable heterogeneous computing systems with autonomous programming and
optimization capabilities, we propose a unified, end-to-end, programmable graph
representation learning (PGL) framework that is capable of mining the
complexity of high-level programs down to the universal intermediate
representation, extracting the specific computational patterns and predicting
which code segments would run best on a specific core in heterogeneous hardware
platforms. The proposed framework extracts multi-fractal topological features
from code graphs, utilizes graph autoencoders to learn how to partition the
graph into computational kernels, and exploits graph neural networks (GNN) to
predict the correct assignment to a processor type. In the evaluation, we
validate the PGL framework and demonstrate a maximum speedup of 6.42x compared
to the thread-based execution, and 2.02x compared to the state-of-the-art
technique.
