F e B R uA RY 2 0 1 2 | Vo L. 5 5 | N o. 2 | c oM M u n i c aT i o n s o f T he ac M 81in performing them for terabyte or larger datasets (increasingly common across scientific disciplines) are quite different from those that applied when data volumes were measured in kilobytes. The result is a computational crisis in many laboratories and a growing need for far more powerful data-management tools, yet the typical researcher lacks the resources and expertise to operate these tools.The answer may be to deliver research data-management capabilities to users as hosted "software as a service," or SaaS, 18 a software-delivery model in which software is hosted centrally and accessed by users using a thin client (such as a Web browser) over the Internet. As demonstrated in many business and consumer tools, SaaS leverages intuitive Web 2.0 in-a S B i g D ata emerges as a force in science, 2,3 so, too, do new, onerous tasks for researchers. Data from specialized instrumentation, numerical simulations, and downstream manipulations must be collected, indexed, archived, shared, replicated, and analyzed. These tasks are not new, but the complexities involved software as a service for Data scientists The costs of research data life-cycle management are growing dramatically as data becomes larger and more complex.saas approaches are a promising solution, outsourcing time-consuming research data management tasks to third-party services.Globus online demonstrates the potential of saas for research data management, simplifying data movement for researchers and research facilities alike.
One contribution of 11 to a theme issue 'X-ray tomographic reconstruction for materials science' . A maximum a posteriori approach is proposed for X-ray diffraction tomography for reconstructing threedimensional spatial distribution of crystallographic phases and orientations of polycrystalline materials. The approach maximizes the a posteriori density which includes a Poisson log-likelihood and an a priori term that reinforces expected solution properties such as smoothness or local continuity. The reconstruction method is validated with experimental data acquired from a section of the spinous process of a porcine vertebra collected at the 1-ID-C beamline of the Advanced Photon Source, at Argonne National Laboratory. The reconstruction results show significant improvement in the reduction of aliasing and streaking artefacts, and improved robustness to noise and undersampling compared to conventional analytical inversion approaches. The approach has the potential to reduce data acquisition times, and significantly improve beamtime efficiency.
Wide-area data transfers in high-performance computing infrastructures are increasingly being carried over dynamically provisioned dedicated network connections that provide high capacities with no competing tra c. We present extensive TCP throughput measurements and time traces over a suite of physical and emulated 10 Gbps connections with 0-366 ms round-trip times (RTTs). Contrary to the general expectation, they show signi cant statistical and temporal variations, in addition to the overall dependencies on the congestion control mechanism, bu er size, and the number of parallel streams. We analyze several throughput pro les that have highly desirable concave regions wherein the throughput decreases slowly with RTTs, in stark contrast to the convex pro les predicted by various TCP analytical models. We present a generic throughput model that abstracts the ramp-up and sustainment phases of TCP ows, which provides insights into qualitative trends observed in measurements across TCP variants: (i) slow-start followed by wellsustained throughput leads to concave regions; (ii) large bu ers and multiple parallel streams expand the concave regions in addition to improving the throughput; and (iii) stable throughput dynamics, indicated by a smoother Poincaré map and smaller Lyapunov exponents, lead to wider concave regions. ese measurements and analytical results together enable us to select a TCP variant and its parameters for a given connection to achieve high throughput with statistical guarantees.
In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (CMIP-5) experiments, which are designed to answer crucial questions about future regional climate change and the results of carbon feedback for different mitigation scenarios. The CMIP-5 experiments will generate petabytes of data that must be replicated seamlessly, reliably, and quickly to hundreds of research teams around the globe. As an end-to-end test of the technologies that will be used to perform this task, a multidisciplinary team of researchers moved a small portion (10 TB) of the multimodel Coupled Model Intercomparison Project, Phase 3 data set used in the IPCC Fourth Assessment Report from three sources-the Argonne Leadership Computing Facility (ALCF), Lawrence Livermore National Laboratory (LLNL) and National Energy Research Scientific Computing Center (NERSC)-to the 2009 Supercomputing conference (SC09) show floor in Portland, Oregon, over circuits provided by DOE's ESnet. The team achieved a sustained data rate of 15 Gb/s on a 20 Gb/s network. More important, this effort provided critical feedback on how to deploy, tune, and monitor the middleware that will be used to replicate the upcoming petascale climate datasets. We report on obstacles overcome and the key lessons learned from this successful bandwidth challenge effort.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.