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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No significant increase in the prevalence of lumbar spondylolysis was demonstrated in patients older than 20 years. This suggests that the development of symptomatic lumbar pars defects do not occur in this population and should not be considered as a rare but potentially treatable cause of new onset low back pain in adults. This study demonstrated an overall prevalence of pars defects of 8.0% in our population. As demonstrated in previous studies, the male to female ratio of 1.5:1 was a statistically significant difference.

The remote visual exploration of live data generated by scientific simulations is useful for scientific discovery, performance monitoring, and online validation for the simulation results. Online visualization methods are challenged, however, by the continued growth in the volume of simulation output data that has to be transferred from its source -the simulation running on the high end machine -to where it is analyzed, visualized, and displayed. A specific challenge in this context is limits in the communication bandwidth between data source(s) and sinks. Previous work places queries 'near' data sources, exploiting their data reduction capabilities, but such work does not address the common scenario in which scientists make multiple different queries on the data being produced. This paper considers the general case in which science users are interested in different (sub)sets of the data produced by a high end simulation. We offer the FlexQuery online data query system that can deploy and execute data queries 'along' the I/O and analytics pipelines. FlexQuery carefully extends such analytics pipelines, using online performance monitoring and data location tracking, to realize data queries in ways that minimize additional data movement and offer low latency in data query execution. Using a real-world scientific application -the Maya astrophysics code and its analytics workflow -we demonstrate FlexQuery's ability to dynamically deploy queries for low-latency remote data visualization.