​​GraphML News: PyG + NVIDIA, Breakthrough Prize

🚀 PyG announced the release of pyg-lib, the result of collaboration with NVIDIA on speeding up most important PyG operations. It is a low-level GNN library that integrates cuGraph, cuDF, and CUTLASS that improve the speed of matrix multiplications and graph sampling (a common bottleneck when working on large graphs). The reported speedups are pretty astounding - up to x150 when sampling on a GPU. There will be more exciting news about PyG at the upcoming Stanford Graph Learning Workshop!

👏 Breakthrough Prize (renowned as the “Oscars of Science”) announced the winners in life sciences, maths, and physics - graph and geometry areas are well represented there!

- John Jumper (DeepMind) and Demis Hassabis (DeepMind) received the Life Sciences prize for AlphaFold
- Daniel A. Spielman (Yale University) received the Math prize for contributions to spectral graph theory, the Kadison-Singer problem, optimization, and coding theory
- Ronen Eldan (Weizmann Institute of Science and Microsoft Research) received the New Horizons in Mathematics Prize for advancing high-dimensional geometry and probability including the KLS conjecture
- Vera Traub (Uni Bonn PhD 2020) received the Maryam Mirzakhani New Frontiers Prize for advances in approximation results in classical combinatorial optimization problems, including the traveling salesman problem and network design.