When something goes wrong while you're running a program on your personal computer, the worst outcome is typically a reboot and the loss of any unsaved work. But when an application crashes on a supercomputer, the consequences can be much more dramatic. In his talk at the Computation Institute, Argonne's Franck Cappello discussed new resilience strategies for the next era of supercomputing.


More and more industries now use modeling and simulation as critical tools for engineering and design. But as the detail and scale of these simulations grows larger and larger, many companies hit a computational ceiling, unable to perform these advanced calculations as quickly as needed. With a boost from the Chicago Innovation Exchange, Parallel.Works, a new startup company from CI scientists, hopes to provide industries with parallel computing solutions to break through this barrier with a minimum of fuss.


Our new Discovery Engines: Under The Hood workshop series offers practical, hands-on instruction with new and popular computational and data tools. Watch video from the first workshop, Statistical Learning with Python and pandas, to learn about how to use the programming language and library for simplifying data structure and analysis.


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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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