id: 05731388
dt: a
an: 05731388
au: Lastovetsky, Alexey; Reddy, Ravi
ti: Distributed data partitioning for heterogeneous processors based on partial
    estimation of their functional performance models.
so: Lin, Hai-Xiang (ed.) et al., Euro-Par 2009 ‒ parallel processing
    workshops. HPPC, HeteroPAR, PROPER, ROIA, UNICORE, VHPC, Delft, The
    Netherlands, August 25‒28, 2009. Revised selected papers. Berlin:
    Springer (ISBN 978-3-642-14121-8/pbk). Lecture Notes in Computer
    Science 6043, 91-101 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: distributed algorithms; data partitioning algorithms; functional
    performance models; heterogeneous platforms
ci: 
li: doi:10.1007/978-3-642-14122-5_13
ab: Summary: The paper presents a new data partitioning algorithm for parallel
    computing on heterogeneous processors. Like traditional functional
    partitioning algorithms, the algorithm assumes that the speed of the
    processors is characterized by speed functions rather than speed
    constants. Unlike the traditional algorithms, it does not assume the
    speed functions to be given. Instead, it uses a computational kernel to
    estimate the speed functions of the processors for different problem
    sizes during its execution. This makes the algorithm distributed as its
    execution involves all the heterogeneous processors. The algorithm does
    not construct the complete speed function for each processor but rather
    builds and uses their partial estimates sufficient for optimal data
    distribution with a given accuracy. The low execution cost of this
    algorithm makes it ideal for employment in self-adaptable applications.
    Experiments with a parallel matrix multiplication application employing
    this algorithm are performed on a local heterogeneous computational
    cluster. The results show that the algorithm converges very fast and
    that its execution time is several orders of magnitude less than the
    total execution time of the application.
rv: