Replication for send-deterministic MPI HPC applications

International audienceReplication has recently gained attention in the context of fault tolerance for large scale MPI HPC applications. Existing implementations try to cover all MPI codes and to be independent from the underlying library. In this paper, we evaluate the advantages of adopting a different approach. First, we try to take advantage of a communication property common to many MPI HPC application, namely send-determinism. Second, we choose to implement replication inside the MPI library. The main advantage of our approach is simplicity. While being only a small patch to the Open MPI library, our solution called SDR-MPI supports most main features of the MPI standard including all collectives and group operations. SDR-MPI additionally achieves good performance: Experiments run with HPC benchmarks and applications show that its overhead remains below 5%

Reproducible and User-Controlled Software Environments in HPC with Guix

Support teams of high-performance computing (HPC) systems often find themselves between a rock and a hard place: on one hand, they understandably administrate these large systems in a conservative way, but on the other hand, they try to satisfy their users by deploying up-to-date tool chains as well as libraries and scientific software. HPC system users often have no guarantee that they will be able to reproduce results at a later point in time, even on the same system-software may have been upgraded, removed, or recompiled under their feet, and they have little hope of being able to reproduce the same software environment elsewhere. We present GNU Guix and the functional package management paradigm and show how it can improve reproducibility and sharing among researchers with representative use cases.Comment: 2nd International Workshop on Reproducibility in Parallel Computing (RepPar), Aug 2015, Vienne, Austria. http://reppar.org

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications’ output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.This work was supported by the STIC-AmSud/CAPES scientific cooperation program under the EnergySFE research project grant 99999.007556/2015-02, EU H2020 Programme, and MCTI/RNP-Brazil under the HPC4E Project, grant agreement n° 689772. Tested K40 boards were donated thanks to Steve Keckler, Timothy Tsai, and Siva Hari from NVIDIA.Postprint (author's final draft

Overcoming extreme-scale reproducibility challenges through a unified, targeted, and multilevel toolset

pre-printReproducibility, the ability to repeat program executions with the same numerical result or code behavior, is crucial for computational science and engineering applications. However, non-determinism in concurrency scheduling often hampers achieving this ability on high performance computing (HPC) systems. To aid in managing the adverse effects of non-determinism, prior work has provided techniques to achieve bit-precise reproducibility, but most of them focus only on small-scale parallelism. While scalable techniques recently emerged, they are disparate and target special purposes, e.g., single-schedule domains. On current systems with O(106) compute cores and future ones with O(109), any technique that does not embrace a unied, targeted, and multilevel approach will fall short of providing reproducibility. In this paper, we argue for a common toolset that embodies this approach, where programmers select and compose complementary tools and can effectively, yet scalably, analyze, control, and eliminate sources of non-determinism at scale. This allows users to gain reproducibility only to the levels demanded by specific code development needs. We present our research agenda and ongoing work toward this goal

Overcoming extreme-scale reproducibility challenges through a unified, targeted, and multilevel toolset

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