Supporting Hybrid MPI and OpenSHMEM over InfiniBand: Design and Performance Evaluation

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Abstract

Message Passing Interface (MPI) has been the defacto programming model for scientific parallel applications. However, data driven applications with irregular communication patterns are harder to implement using MPI. The Partitioned Global Address Space (PGAS) programming models present an alternative approach to improve programmability. Open SHMEM is a library-based implementation of the PGAS model and it aims to standardize the SHMEM model to achieve performance, programmability and portability. However, Open SHMEM is an emerging standard and it is unlikely that entire an application will be re-written with it. Instead, it is more likely that applications will continue to be written with MPI as the primary model, but parts of them will be re-designed with newer models. This requires the underlying communication libraries to be designed with support for multiple programming models. In this paper, we propose a high performance, scalable unified communication library that supports both MPI and Open SHMEM for InfiniBand clusters. To the best of our knowledge, this is the first effort in unifying MPI and Open SHMEM communication libraries. Our proposed designs take advantage of InfiniBand's advanced features to significantly improve the communication performance of various atomic and collective operations defined in Open SHMEM specification. Hybrid (MPI+Open SHMEM) parallel applications can benefit from our proposed library to achieve better efficiency and scalability. Our studies show that our proposed designs can improve the performance of OpenSHMEM's atomic operations and collective operations by up to 41%. We observe that our designs improve the performance of the 2D-Heat Modeling benchmark (pure Open-SHMEM) by up to 45%. We also observe that our unified communication library can improve the performance of the hybrid (MPI+Open SHMEM) version of Graph500 benchmark by up to 35%. Moreover, our studies also indicate that our proposed designs lead to lower memory consumption due to efficient utilization of the network resources.

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