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The first exa-scale supercomputers are expected to be operational in China, USA, Japan and Europe within the early 2020’s. This allows scientists to execute applications at extreme scale with more than 1018 floating point operations per second (exa-FLOPS). However, the number of FLOPS is not the only parameter that determines the final performance. In order to store intermediate results or to provide fault tolerance, most applications need to perform a considerable amount of I/O operations during runtime. The performance of those operations is determined by the throughput from volatile (e.g. DRAM) to non-volatile stable storage. Regarding the slow growth in network bandwidth compared to the computing capacity on the nodes, it is highly beneficial to deploy local stable storage such as the new non-volatile memories (NVMe), in order to avoid the transfer through the network to the parallel file system. In this work, we analyse the performance of three different storage levels of the CTE-POWER9 cluster, located at the Barcelona Supercomputing Center (BSC). We compare the throughputs of SSD, NVMe on the nodes to the GPFS under various scenarios and settings. We measured a maximum performance on 16 nodes of 83 GB/s using NVMe devices, 5.6 GB/s for SSD devices and 4.4 GB/s for writes to the GPFS. This project has received funding from the European Union’sHorizon 2020 research and innovation programme under the Marie Sklodowska-Curiegrant agreement No 708566 (DURO). Part of the research presented here has receivedfunding from the European Union’s Seventh Framework Programme (FP7/2007-2013)and the Horizon 2020 (H2020) funding framework under grant agreement no. H2020-FETHPC-754304 (DEEP-EST). The present publication reflects only the authors’views. The European Commission is not liable for any use that might be made ofthe information contained therein.

High-performance computing (HPC) requires resilience techniques such as checkpointing in order to tolerate failures in supercomputers. As the number of nodes and memory in supercomputers keeps on increasing, the size of checkpoint data also increases dramatically, sometimes causing an I/O bottleneck. Differential checkpointing (dCP) aims to minimize the checkpointing overhead by only writing data differences. This is typically implemented at the memory page level, sometimes complemented with hashing algorithms. However, such a technique is unable to cope with dynamic-size datasets. In this work, we present a novel dCP implementation with a new file format that allows fragmentation of protected datasets in order to support dynamic sizes. We identify dirty data blocks using hash algorithms. In order to evaluate the dCP performance, we ported the HPC applications xPic, LULESH 2.0 and Heat2D and analyze them regarding their potential of reducing I/O with dCP and how this data reduction influences the checkpoint performance. In our experiments, we achieve reductions of up to 62% of the checkpoint time., This project has received funding from the European Unions Seventh Framework Programme (FP7/2007-2013) and the Horizon 2020 (H2020) funding framework under grant agreement no. H2020-FETHPC-754304 (DEEP-EST); and the LEGaTO Project (legato- project.eu), grant agreement No 780681

The coming exascale era is a great opportunity for high performance computing (HPC) applications. However, high failure rates on these systems will hazard the successful completion of their execution. Bit-flip errors in dynamic random access memory (DRAM) account for a noticeable share of the failures in supercomputers. Hardware mechanisms, such as error correcting code (ECC), can detect and correct single-bit errors and can detect some multi-bit errors while others can go undiscovered. Unfortunately, detected multi-bit errors will most of the time force the termination of the application and lead to a global restart. Thus, other strategies at the software level are needed to tolerate these type of faults more efficiently and to avoid a global restart. In this work, we extend the FTI checkpointing library to facilitate the implementation of custom recovery strategies for MPI applications, minimizing the overhead introduced when coping with soft errors. The new functionalities are evaluated by implementing local forward recovery on three HPC benchmarks with different reliability requirements. Our results demonstrate a reduction on the recovery times by up to 14%. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 708566 (DURO). This research is also supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the EU (Projects TIN2016-75845-P and the predoctoral grant of Nuria Losada ref. BES-2014-068066), and by the Galician Government (Xunta de Galicia) under the Consolidation Program of Competitive Research (ref. ED431C 2017/04).

The efficient utilization of current supercomputing systems with deep storage hierarchies demands scientific applications that are capable of leveraging such heterogeneous hardware. Fault tolerance, and checkpointing in particular, is one of the most time-consuming aspects if not handled correctly. High checkpoint performance can be achieved using optimized multilevel checkpoint and restart libraries. Unfortunately, those libraries do not allow for restarts with a modified number of processes or scientific post-processing of the checkpointed data. This is because they typically use an N-N checkpointing scheme and opaque file-formats. In this article, we present a novel mechanism to asynchronously store checkpoints into a self-descriptive file format and load the data upon recovery with a different number of processes. We provide an API that defines the process-local data as part of a globally shared dataset. Our measurements demonstrate a low overhead between 0.6% and 2.5% for a 2.25 TB checkpoint with 6K processes. Part of the research presented here has received funding from the Horizon 2020 (H2020) funding framework under grant/award number: 824158; Energy oriented Centre of Excellence II (EoCoE-II). The present publication reflects only the authors’ views. The European Commission is not liable for any use that might be made of the information contained therein. This work was partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DEAC52-07NA2734 (LLNLCONF-811845).