As we approach the exascale era in high-performance computing, the need for innovative algorithms that can efficiently handle massive datasets and complex computations is increasing. The CBM4scale innovation study focuses on the development of a novel matrix compression format and associated algorithms to improve the performance of Graph Neural Networks (GNNs) and other scientific applications on exascale systems. In a recent interview, Prof. Dr. Siegfried Benkner, co-principal investigator of CBM4scale and head of the Scientific … Continue reading CBM4scale: Transforming Graph Neural Networks with Compressed Binary Matrix Algorithms to Advance Exacale Computing