Bridges is being built to facilitate research ranging from traditional HPC areas like astronomy and physics  through emerging fields like genomics to decision science, natural language processing and digital humanities.   Bridges could be a good fit for you if:

You want to scale your research beyond the limits of your laptop, using familiar software and user environments.

You want to collaborate with other researchers with complementary expertise. 

Your  research can take advantage of any of the following:

  • Rich data collections - Rapid access to data collections will support their use by individuals, collaborations and communities.
  • Cross-domain analyses - Concurrent access to datasets from different sources, along with tools for their integration and fusion, will enable new kinds of questions.
  • Gateways and workflows - Web portals will provide intuitive access to complex workflows that run "behind the scenes". 
  • Large coherent memory - Bridges' 3TB and 12TB nodes will be ideal for memory-intensive applications, such as genomics and machine learning.
  • In-memory databases  - Bridges' large-memory nodes will be valuable for in-memory databases, which are important due to their performance advantages.
  • Graph analytics - Bridges' hardware-enabled shared memory nodes wil execute algorithms for large, nonpartitionable graphs and complex data very efficiently.
  • Optimization and parameter sweeps - Bridges is designed to run large numbers of small to moderate jobs extremely well, making it ideal for large-scale optimization problems.
  • Rich software environments - Robust collections of applications and tools, for example in statistics, machine learning and natural language processing, will allow researchers to focus on analysis rather than coding. 
  • Data-intensive workflows - Bridges' filesystems and high bandwidth will provide strong support for applications that are typically I/O bandwidth-bound.  One example is an analysis that runs best with steps expressed in different programming models, such as data cleaning and summarization with Hadoop-based tools, followed by graph algorithms that run more efficiently with shared memory. 
  • Contemporary applications - Applications written in Java, Python, R, MATLAB, SQL, C++, C, Fortran, MPI, OpenACC, CUDA and other popular languages will run naturally on Bridges.