SCHEDULE: NOV 10-16, 2012
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Build to Order Linear Algebra Kernels
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Christopher Gropp, Geoffrey Belter, Elizabeth Jessup, Thomas Nelson, Boyana Norris, Jeremy Siek
ROOM:East Entrance
ABSTRACT:
Tuning linear algebra kernels for specific machines is a time-consuming
and difficult process. The Build-to-Order (BTO) compiler takes
MATLAB-like kernels and generates fully tuned C code for the machine on
which it runs. BTO allows users to create optimized linear algebra
kernels customized to their needs and machines. Previous work has shown
that BTO kernels perform competitively with or better than hand-tuned
code. We now test BTO on a full application. We have selected
bidiagonalization, a non-trivial matrix operation useful in computing
singular value decompositions. We use BTO to tune the core kernels of
the algorithm, rather than the entire application. This poster shows the
comparative performance of four implementations of bidiagonalization:
the LAPACK routine DGEBD2, a BLAS 2.5 based algorithm developed by
Howell et al., and those same making use of BTO kernels.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Christopher Gropp - Rose-Hulman Institute of Technology
Geoffrey Belter - University of Colorado Boulder
Elizabeth Jessup - University of Colorado Boulder
Thomas Nelson - University of Colorado Boulder
Boyana Norris - Argonne National Laboratory
Jeremy Siek - University of Colorado Boulder
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Build to Order Linear Algebra Kernels
SESSION: Research Poster Reception
EVENT TYPE:
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Christopher Gropp, Geoffrey Belter, Elizabeth Jessup, Thomas Nelson, Boyana Norris, Jeremy Siek
ROOM:East Entrance
ABSTRACT:
Tuning linear algebra kernels for specific machines is a time-consuming
and difficult process. The Build-to-Order (BTO) compiler takes
MATLAB-like kernels and generates fully tuned C code for the machine on
which it runs. BTO allows users to create optimized linear algebra
kernels customized to their needs and machines. Previous work has shown
that BTO kernels perform competitively with or better than hand-tuned
code. We now test BTO on a full application. We have selected
bidiagonalization, a non-trivial matrix operation useful in computing
singular value decompositions. We use BTO to tune the core kernels of
the algorithm, rather than the entire application. This poster shows the
comparative performance of four implementations of bidiagonalization:
the LAPACK routine DGEBD2, a BLAS 2.5 based algorithm developed by
Howell et al., and those same making use of BTO kernels.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Christopher Gropp - Rose-Hulman Institute of Technology
Geoffrey Belter - University of Colorado Boulder
Elizabeth Jessup - University of Colorado Boulder
Thomas Nelson - University of Colorado Boulder
Boyana Norris - Argonne National Laboratory
Jeremy Siek - University of Colorado Boulder
Click here to download .ics calendar file