A Proposed Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (FAMLIES)
This work addresses the need for flexible, high-performance linear algebra and tensor operations in engineering and science, but it is incremental as it builds on existing efforts.
The authors propose a new framework called FAMLIES that vertically integrates dense linear and multi-linear (tensor) software stacks to enable high-performance computations across CPU and GPU architectures, building on prior projects like BLIS and libflame.
We leverage highly successful prior projects sponsored by multiple NSF grants and gifts from industry: the BLAS-like Library Instantiation Software (BLIS) and the libflame efforts to lay the foundation for a new flexible framework by vertically integrating the dense linear and multi-linear (tensor) software stacks that are important to modern computing. This vertical integration will enable high-performance computations from node-level to massively-parallel, and across both CPU and GPU architectures. The effort builds on decades of experience by the research team turning fundamental research on the systematic derivation of algorithms (the NSF-sponsored FLAME project) into practical software for this domain, targeting single and multi-core (BLIS, TBLIS, and libflame), GPU-accelerated (SuperMatrix), and massively parallel (PLAPACK, Elemental, and ROTE) compute environments. This project will implement key linear algebra and tensor operations which highlight the flexibility and effectiveness of the new framework, and set the stage for further work in broadening functionality and integration into diverse scientific and machine learning software.