id: 06104385
dt: a
an: 06104385
au: Luszczek, Piotr; Dongarra, Jack
ti: Reducing the time to tune parallel dense linear algebra routines with
    partial execution and performance modeling.
so: Wyrzykowski, Roman (ed.) et al., Parallel processing and applied
    mathematics. 9th international conference, PPAM 2011, Torun, Poland,
    September 11‒14, 2011. Revised selected papers, Part I. Berlin:
    Springer (ISBN 978-3-642-31463-6/pbk). Lecture Notes in Computer
    Science 7203, 730-739 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: Linear systems; parallel algorithms; modeling techniques
ci: 
li: doi:10.1007/978-3-642-31464-3_74
ab: Summary: We present a modeling framework to accurately predict time to run
    dense linear algebra calculation. We report the framework’s accuracy
    in a number of varied computational environments such as shared memory
    multicore systems, clusters, and large supercomputing installations
    with tens of thousands of cores. We also test the accuracy for various
    algorithms, each of which having a different scaling properties and
    tolerance to low-bandwidth/high-latency interconnects. The predictive
    accuracy is very good and on the order of measurement accuracy which
    makes the method suitable for both dedicated and non-dedicated
    environments. We also present a practical application of our model to
    reduce the time required to tune and optimize large parallel runs whose
    time is dominated by linear algebra computations. We show practical
    examples of how to apply the methodology to avoid common pitfalls and
    reduce the influence of measurement errors and the inherent performance
    variability.
rv: