A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures
Journal of the ACM (JACM)
Dynamic Load Balancing and Efficient Load Estimators for Asynchronous Iterative Algorithms
IEEE Transactions on Parallel and Distributed Systems
Building the functional performance model of a processor
Proceedings of the 2006 ACM symposium on Applied computing
Data Partitioning with a Functional Performance Model of Heterogeneous Processors
International Journal of High Performance Computing Applications
Dynamic Load Balancing on Dedicated Heterogeneous Systems
Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
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High performance of data-parallel applications on heterogeneous platforms can be achieved by partitioning the data in proportion to the speeds of processors. It has been proven that the speed functions built from a history of time measurements better reflect different aspects of heterogeneity of processors. However, existing data partitioning algorithms based on functional performance models impose some restrictions on the shape of speed functions, which are not always satisfied if we try to approximate the real-life measurements accurately enough. This paper presents a new data partitioning algorithm that applies multidimensional solvers to numerical solution of the system of non-linear equations formalizing the problem of optimal data partitioning. This algorithm relaxes the restrictions on the shape of speed functions and uses the Akima splines for more accurate and realistic approximation of the real-life speed functions. The better accuracy of the approximation in its turn results in a more optimal distribution of the computational load between the heterogeneous processors.