Abstract. A large number of MPI implementations are currently available, each of which emphasize different aspects of high-performance computing or are intended to solve a specific research problem. The result is a myriad of incompatible MPI implementations, all of which require separate installation, and the combination of which present significant logistical challenges for end users. Building upon prior research, and influenced by experience gained from the code bases of the LAM/MPI, LA-MPI, and FT-MPI projects, Open MPI is an all-new, productionquality MPI-2 implementation that is fundamentally centered around component concepts. Open MPI provides a unique combination of novel features previously unavailable in an open-source, production-quality implementation of MPI. Its component architecture provides both a stable platform for third-party research as well as enabling the run-time composition of independent software add-ons. This paper presents a high-level overview the goals, design, and implementation of Open MPI.

Previous studies of application usage show that the performance of collective communications are critical for high-performance computing. Despite active research in the field, both general and feasible solution to the optimization of collective communication problem is still missing.In this paper, we analyze and attempt to improve intracluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communication, such as Hockney, LogP/LogGP, and PLogP, to collective operations. We compare the predictions from models against the experimentally gathered data and using these results, construct optimal decision function for broadcast collective. We quantitatively compare the quality of the model-based decision functions to the experimentally-optimal one. Addi-J. Pješivac-Grbović ( ) · T. Angskun · G. tionally, in this work, we also introduce a new form of an optimized tree-based broadcast algorithm, splitted-binary.Our results show that all of the models can provide useful insights into various aspects of the different algorithms as well as their relative performance. Still, based on our findings, we believe that the complete reliance on models would not yield optimal results. In addition, our experimental results have identified the gap parameter as being the most critical for accurate modeling of both the classical point-topoint-based pipeline and our extensions to fan-out topologies.

Initial versions of MPI were designed to work efficiently on multiprocessors which had very little job control and thus static process models, subsequently forcing them to support dynamic process operations would have effected their performance. As current HPC systems increase in size with higher potential levels of individual node failure, the need rises for new fault tolerant systems to be developed. Here we present a new implementation of MPI called FT-MPI 1 that allows the semantics and associated failure modes to be completely controlled by the application. Given is an overview of the FT-MPI semantics, design and some performance issues as well as the HARNESS g_hcore implementation it is built upon.

The performance of the MPI's collective communications is critical in most MPI-based applications. A general algorithm for a given collective communication operation may not give good performance on all systems due to the differences in architectures, network parameters and the storage capacity of the underlying MPI implementation. In this paper, we discuss an approach in which the collective communications are tuned for a given system by conducting a series of experiments on the system. We also discuss a dynamic topology method that uses the tuned static topology shape, but re-orders the logical addresses to compensate for changing run time variations. A series of experiments were conducted comparing our tuned collective communication operations to various native vendor MPI implementations. The use of the tuned collective communications resulted in about 30%-650% improvement in performance over the native MPI implelementations.

As the number of processors in today's high performance computers continues to grow, the mean-time-to-failure of these computers are becoming significantly shorter than the execution time of many current high performance computing applications. Although today's architectures are usually robust enough to survive node failures without suffering complete system failure, most today's high performance computing applications can not survive node failures and, therefore, whenever a node fails, have to abort themselves and restart from the beginning or a stable-storage-based checkpoint.This paper explores the use of the floating-point arithmetic coding approach to build fault survivable high performance computing applications so that they can adapt to node failures without aborting themselves. Despite the use of erasure codes over Galois field has been theoretically attempted before in diskless checkpointing, few actual implementations exist. This probably derives from concerns related to both the efficiency and the complexity of implementing such codes in high performance computing applications. In this paper, we introduce the simple but efficient floating-point arithmetic coding approach into diskless checkpointing and address the associated round-off error issue. We also implement a floating-point arithmetic version of the Reed-Solomon coding scheme into a conjugate gradient equation solver and evaluate both the performance and the numerical impact of this scheme. Experimental results demonstrate that the proposed floating-point arithmetic coding approach is able to survive a small number of simultaneous node failures with low performance overhead and little numerical impact. *