Los Alamos National Laboratory

Los Alamos National Laboratory

Delivering science and technology to protect our nation and promote world stability

Applied Computer Science

Innovative co-design of applications, algorithms, and architectures in order to enable scientific simulations at extreme scale


  • Professional Assistant
  • Erika Maestas
  • (505) 664-0673
  • Email

Basic and applied research supporting national security science

We are the vanguard for scientific simulations at extreme scale through the co-design of applications, algorithms, and architectures.

Research Areas
  • Co-design
  • Data science at scale
  • Scientific visualization
  • Programming models

Ben Bergen, Team Leader
Irina Demeshko, Deputy Team Leader

The Co-Design team concentrates on the optimization of entire computing systems--from the application to the hardware. We use an agile co-design process of rapid iteration through the problem space based largely on the use of proxy applications.

Whenever possible these proxy applications are released as open source codes to facilitate collaboration with academic and industrial partners.

Our team members have experience and expertise in

  • programming models and languages,
  • runtime systems,
  • Monte Carlo techniques,
  • functional languages, and
  • advanced hardware architectures including
    • CPUs,
    • GPUs, and,most notably,
    • FPGA-based systems.
Data Science at Scale

David Rogers, Team Leader
John Patchett, Deputy Team Leader

Scientific Visualization is an essential tool for understanding the vast quantities of large-scale, time-dependent data produced from high performance computer simulations.

While interaction is recognized as a key feature for useful exploration of scientific data, sufficient speed for interaction is impossible on these large data sets using commercially available visualization software and algorithms. Therefore, an extensive research program is required to meet future requirements.

The nature of the required research spans the areas of traditional computer graphics, scientific visualization and computer systems software.

Future Architectures and Applications

Rob Aulwes, Team Leader
Louis Vernon, Deputy Team Leader

The focus of the Future Architectures and Applications Team is to work with domain scientists and their applications to take the best advantage of current and forthcoming supercomputer architectures and to leverage knowledge of advanced architectures and computing at extreme scales. We work extensively with open science projects through the Institutional Computing Program and with the Weapons Program through the ASC Program, as well as with other computing projects throughout the laboratory. Our goal is to keep science at LANL on the forefront of the rapidly changing supercomputing landscape.

Much of our work is educational in nature. We keep up to date on advanced computing hardware (such as multi- and many-core CPUs, GPUs, and various accelerators) and software (MPI, OpenMP, OpenACC, OpenCL, CUDA, etc.). This knowledge is transferred to code teams and implemented in codes through user group meetings, workshops, and by working directly with project teams. We supply in-depth consulting to applications teams so their applications run efficiently on current and forthcoming computer architectures. Central to this work is understanding and modifying applications to expose parallelism and vectorization opportunities, essential to effective use of modern computer architectures.

Programming Models

Patrick McCormick, Team Leader
Christine Sweeney, Deputy Team Leader

The Programming Models team bridges the gap between underlying hardware architectures and the supporting layers of software available to applications. This includes a range of topics from programming languages, supporting compiler infrastructures, runtime software, and application programming interfaces.

Our overall goal leverages all of these activities with a goal of increasing developer productivity and understanding of the interactions between software and hardware. We are driven by challenging applications in a number of areas ranging from computational physics as well as data-intensive Computing.