  In recent years, Graph Neural Networks (GNNs) have ignited a surge of
innovation, significantly enhancing the processing of geometric data structures
such as graphs, point clouds, and meshes. As the domain continues to evolve, a
series of frameworks and libraries are being developed to push GNN efficiency
to new heights. While graph-centric libraries have achieved success in the
past, the advent of efficient tensor compilers has highlighted the urgent need
for tensor-centric libraries. Yet, efficient tensor-centric frameworks for GNNs
remain scarce due to unique challenges and limitations encountered when
implementing segment reduction in GNN contexts.
  We introduce GeoT, a cutting-edge tensor-centric library designed
specifically for GNNs via efficient segment reduction. GeoT debuts innovative
parallel algorithms that not only introduce new design principles but also
expand the available design space. Importantly, GeoT is engineered for
straightforward fusion within a computation graph, ensuring compatibility with
contemporary tensor-centric machine learning frameworks and compilers. Setting
a new performance benchmark, GeoT marks a considerable advancement by
showcasing an average operator speedup of 1.80x and an end-to-end speedup of
1.68x.
