Los Alamos National LaboratoryIS&T Co-Design Summer School
Train future scientists to work across disciplines to solve today's challenges

Information Science and Technology Center (IS&T) Co-Design Summer School

The Los Alamos IS&T Co-Design Summer School was created to train future scientists to work on the kinds of interdisciplinary teams that are demanded by today’s challenges. Launched in 2011, the summer school recruits top candidates in a range of fields spanning domain science, applied mathematics, computational and computer science, and computer architecture. These students work together to solve a focused problem that is designed to build the skills they will need to tackle the grand challenges of the future. Foremost among these skills is the ability to work across disciplines with other team members, while employing their own unique expertise. This is the heart of co-design.

Past summer school challenges have included problems in kinetic theory (Boltzmann Transport Equation), molecular dynamics and hydrodynamics

For more information about the 2015 Co-Design Summer School Application Process.

2014 Summer School

The driving problem of the 2014 summer school is the lack of a common infrastructure in current simulation codes developed at the Laboratory. Most of the current simulation codes are developed in the same set of languages and operate on a base mesh/grid structure. While most applications use similar data structures, there is no common implementation or well-defined APIs. Furthermore, any additional features, such as the ability to adaptively refine or load balance, must be implemented individually by the developers.

To combat these problems, the goal of the summer school is to develop APIs for quadtree (2D) or octree (3D) adaptive mesh data structures. These APIs will be used to implement an adaptive mesh refinement, finite volume hydrodynamics solver for the Sod shock tube problem, and potentially additional applications. The applications will be released as open source proxy code.

In addition, these will be implemented in a variety of runtime systems and the performance will be analyzed with a focus on current problems in scientific computing.