summary: improve prediction of run-time performance of a computation graph using GNN, they use an embedding dictionary for each node's opcode along with some ot

summary: improve prediction of run-time performance of a computation graph using GNN, they use an embedding dictionary for each node's opcode along with some other node features (eg shape, bits, window size, see [1]), they released a big dataset of these graphs in [2] with varying XLA compilation configurations and their resulting perf on TPUs, they did some stuff to improve prediction on bigger graphs than before in [3] by partitioning the graph (METIS graph partition, new to me) and other training things This is only about predicting performance of a given graph and not about improving/suggesting/editing a new equivalent graph. As in FunSearch, models which have decent predictive power could be used with evolutionary search. [1] https://github.com/google-research-datasets/tpu_graphs#features https://github.com/google-research-datasets/tpu_graphs#featu... [2] TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs https://arxiv.org/abs/2308.13490 https://arxiv.org/abs/2308.13490 [3] Learning Large Graph Property Prediction via Graph Segment Training https://arxiv.org/abs/2305.12322 https://arxiv.org/abs/2305.12322