A LogP Extension for Modeling Tree Aggregation Networks

As high-performance systems continue to expand in power and size, scalable communication and data transfer is necessary to facilitate next generation monitoring and analysis. Many popular frameworks such as MapReduce, MPI and MRNet utilize scalable reduction operations to fulfill the performance requirements of a large distributed system. The structures to handle these aggregations may simply consist of a single level with children reporting directly to the parent node, or it may be layered to create a large tree with varying breadth and height. Despite their common-place, the techniques for modeling these Tree Aggregation Networks (TANs) are lacking. This paper addresses this need by introducing a novel extension of the LogP framework for Tree Aggregation Networks. Our TAN model adheres to the simplicity of the LogP model, but utilizes structural insights to provide a simple yet precise performance estimate. Additionally, our model makes no assumptions of the underlying NIC transfer mechanisms or uniformity of tree breadth, making it suitable for a wide range of environments. To evaluate our TAN model, we compare it against the traditional LogP model for predicting the performance of the Multicast Reduction Network (MRNet) framework.