A Proposed Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (FAMLIES)

TL;DR

This paper proposes a flexible, vertically integrated software framework for high-performance linear algebra and tensor computations across diverse hardware architectures, building on prior successful projects.

Contribution

It introduces a new framework that unifies dense linear and multi-linear software stacks, enabling efficient computations from node-level to massively parallel systems.

Findings

Framework demonstrates high performance on CPU and GPU architectures.

Implementation of key linear algebra and tensor operations showcases flexibility.

Lays groundwork for broader scientific and machine learning software integration.

Abstract

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.