Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Auto-tuning the Matrix Powers Kernel with SEJITS

Jeffrey Morlan

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2012-95
May 11, 2012

http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-95.pdf

The matrix powers kernel, used in communication-avoiding Krylov subspace methods, requires runtime auto-tuning for best performance. We demonstrate how the SEJITS (Selective Embedded Just-In-Time Specialization) approach can be used to deliver a high-performance and performance-portable implementation of the matrix powers kernel to application authors, while separating their high-level concerns from those of auto-tuner implementers involving low-level optimizations. The benefits of delivering this kernel in the form of a specializer, rather than a traditional library, are discussed. Performance of the matrix powers kernel specializer is evaluated in the context of a communication-avoiding conjugate gradient (CA-CG) solver, which compares favorably to traditional CG.

Advisor: Armando Fox


BibTeX citation:

@mastersthesis{Morlan:EECS-2012-95,
    Author = {Morlan, Jeffrey},
    Title = {Auto-tuning the Matrix Powers Kernel with SEJITS},
    School = {EECS Department, University of California, Berkeley},
    Year = {2012},
    Month = {May},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-95.html},
    Number = {UCB/EECS-2012-95},
    Abstract = {The matrix powers kernel, used in communication-avoiding Krylov subspace methods, requires runtime auto-tuning for best performance. We demonstrate how the SEJITS (Selective Embedded Just-In-Time Specialization) approach can be used to deliver a high-performance and performance-portable implementation of the matrix powers kernel to application authors, while separating their high-level concerns from those of auto-tuner implementers involving low-level optimizations. The benefits of delivering this kernel in the form of a specializer, rather than a traditional library, are discussed. Performance of the matrix powers kernel specializer is evaluated in the context of a communication-avoiding conjugate gradient (CA-CG) solver, which compares favorably to traditional CG.}
}

EndNote citation:

%0 Thesis
%A Morlan, Jeffrey
%T Auto-tuning the Matrix Powers Kernel with SEJITS
%I EECS Department, University of California, Berkeley
%D 2012
%8 May 11
%@ UCB/EECS-2012-95
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-95.html
%F Morlan:EECS-2012-95