Known challenges for petascale machines are that (1) the costs of I/O for high performance applications can be substantial, especially for output tasks like checkpointing, and (2) noise from I/O actions can inject undesirable delays into the runtimes of such codes on individual compute nodes. This paper introduces the flexible 'DataStager' framework for data staging and alternative services within that jointly address (1) and (2). Data staging services moving output data from compute nodes to staging or I/O nodes prior to storage are used to reduce I/O overheads on applications' total processing times, and explicit management of data staging offers reduced perturbation when extracting output data from a petascale machine's compute partition. Experimental evaluations of DataStager on the Cray XT machine at Oak Ridge National Laboratory establish both the necessity of intelligent data staging and the high performance of our approach, using the GTC fusion modeling code and benchmarks running on 1000+ processors.

SUMMARYApplications running on leadership platforms are more and more bottlenecked by storage input/output (I/O). In an effort to combat the increasing disparity between I/O throughput and compute capability, we created Adaptable IO System (ADIOS) in 2005. Focusing on putting users first with a service oriented architecture, we combined cutting edge research into new I/O techniques with a design effort to create near optimal I/O methods. As a result, ADIOS provides the highest level of synchronous I/O performance for a number of mission critical applications at various Department of Energy Leadership Computing Facilities. Meanwhile ADIOS is leading the push for next generation techniques including staging and data processing pipelines. In this paper, we describe the startling observations we have made in the last half decade of I/O research and development, and elaborate the lessons we have learned along this journey. We also detail some of the challenges that remain as we look toward the coming Exascale era.

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Salinity is an agro-environmental problem limiting plant growth and development in the arid to semi-arid regions of the world and becomes the predicament of serious concern. Plants exposed to salt stress may undergo osmotic stress, ion toxicity and nutritional imbalance which results in production of reactive oxygen species (ROS). The ability of plants to detoxify radicals under conditions of salt stress is probably the most critical requirement and is determined by multifarious morpho-physiological and biochemical pathways like initial entry of salt to roots, intercellular compartmentation, synthesis of osmoprotectants (sugars, amino acids, proline and upgradation of antioxidant system) that results in maintaining ion homeostasis. This paper also revealed the plant responses to salinity stress with emphasis on physiological and biochemical mechanisms of salt tolerance which may help in interdisciplinary studies to assess the ecological consequence of salt stress. Moreover, the application of potassium helps the plants to cope with the hazardous effects of salinity by improving the morphological, physiological and biochemical attributes.

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The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

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