When the Computation Institute (CI) was founded in 1999, data were still transferred on CD-Rs, the internet was mostly accessed via dial-up modems, and phones were used solely for phone calls. Nearly two decades later, the world is immersed in data, computation, and connected technologies, with dramatic ramifications for science and society.


GlobusWorld 2018 coincided with an important file transfer milestone: 400 petabytes of data moved between Globus endpoints via the service since 2010. But many of the talks, tutorials, and user stories focused instead on what comes after data reaches its destination. Whether it’s enabling the discovery of promising new materials, helping coordinate multi-site research projects in neuroscience and molecular biology, or facilitating campus- and country-wide storage networks, Globus is increasingly a critical behind-the-scenes partner in some of today’s most exciting science.


The University of Chicago is launching the Center for Data and Applied Computing, a research center for developing new methods in computation and data analytics and applying them to ambitious projects across the full spectrum of science and scholarship.


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The Discovery Cloud is CI Director Ian Foster's vision to deliver powerful computational tools and methods to every professional and amateur scientist around the world, fundamentally transforming the ecosystem of science. Globus is the first step towards realizing this vision.

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The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated advanced digital resources and services in the world. It is a single virtual system that scientists can use to interactively share computing resources, data, and expertise.

The OpenAD/F project seeks to develop a modular, open-source tool for the automatic generation of adjoint code from Fortran 95 source code. Discrete adjoint computations are used for sensitivity analysis and to provide the gradients used in geophysical state estimation. Because derivatives are needed with respect to millions or billions of independent variables, finite different approximations are impractical: a gradient computation that would take minutes or hours using an adjoint computation would take months or years using finite differences.

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