In the News

25-26 Sept 2014
12 June 2014
11 Nov 2013
1 Oct 2013
5-6 Sept 2013
20 Aug 2013 New!
FLAME project receives new NSF grant "SHF: Small: From Matrix Computations to Tensor Computations"
8 Aug 2013
SSI Grant CoPI Don Batory receives Most Influential Paper Award for the paper "Evolving Object-Oriented Designs with Refactorings," with coauthor Lance Tokudo
6 Aug 2013
SSI Grant CoPI Jeff Hammonds to receive 2013 IEEE TCSC Young Achievers Award at SC13
5 Aug 2013
FLAME Project receives NSF SHF grant "From Matrix Computations to Tensor Computations"
17 May 2013
FLAME Project receives supplement for NSF SI2 grant
11 Feb 2013
UT Austin funds FLAME related Massive Open Online Course (MOOC) Link
28 Jun 2012
FLAME Project receives three year NSF Software Infrastructure for Sustained Innovation (SI2) grant Link
16 Nov 2011
Texas Instruments and UT Austin collaborate to deliver linear algebra library on TI's high performance multicore DSPs. Link


The objective of the FLAME project is to transform the development of dense linear algebra libraries from an art reserved for experts to a science that can be understood by novice and expert alike. Rather than being only a library, the project encompasses a new notation for expressing algorithms, a methodology for systematic derivation of algorithms, Application Program Interfaces (APIs) for representing the algorithms in code, and tools for mechanical derivation, implementation and analysis of algorithms and implementations.

High Performance Libraries

Three high performance dense linear algebra libraries, each addressing a layer in the linear algebra software stack, have been developed by the team and our collaborators from both academia and industry.

BLIS is a software framework for instantiating high-performance BLAS-like dense linear algebra libraries. BLIS is written in Standard C (mostly ISO C90 with a few C99 extensions) and available under a new/modified/3-clause BSD license.
Get it here
libFLAME is a high performance dense linear algebra library that is the result of the FLAME methodology for systematically developing dense linear algebra libraries. The FLAME methodology is radically different from the LINPACK/LAPACK approach that dates back to the 1970s.
Get it here
Elemental is a framework for distributed-memory dense linear algebra that strives to be both fast and convenient. It combines ideas including: element-wise matrix distributions, object-oriented submatrix tracking, and first-class matrix distributions. Many algorithms use techniques from LAPACK in order to improve numerical stability.
Get it here