Hierarchical Partitioning Algorithm for Scientific Computing on Highly Heterogeneous CPU + GPU Clusters

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Provided by: University College Cork
Topic: Hardware
Format: PDF
Hierarchical level of heterogeneity exists in many modern high performance clusters in the form of heterogeneity between computing nodes, and within a node with the addition of specialized accelerators, such as GPUs. To achieve high performance of scientific applications on these platforms it is necessary to perform load balancing. In this paper, the authors present a hierarchical matrix partitioning algorithm based on realistic performance models at each level of hierarchy. To minimize the total execution time of the application it iteratively partitions a matrix between nodes and partitions these sub-matrices between the devices in a node. This is a self-adaptive algorithm that dynamically builds the performance models at run-time and it employs an algorithm to minimize the total volume of communication.
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