Advanced Research Computing (ARC)

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Our goal is to provide high-performance computing (HPC) capabilities and expertise to scientists for the acceleration and expansion of scientific discovery. 

Photo of Advanced Research Computing (ARC) "Yeti" supercomputing cluster

USGS Yeti Supercomputer in the BLM Data Center

The Advanced Research Computing (ARC) group is part of the Core Science Systems (CSS) mission area under the Science Analytics and Synthesis (SAS) program. We provide training, consulting, and access to high performance computing resources, such as our USGS Yeti supercomputer. We also work with other groups in the USGS as part of a larger community of practice called the Advanced Computing Cooperative (ACC) that works across USGS organizational boundaries to coordinate advanced computing capabilities and expertise for USGS scientists.

Machine Access
Need more computing power?

We provide access to our in-house HPC cluster “Yeti”, modelled after world-class supercomputers.

Consulting and Training
How do I learn to use a supercomputer?

We provide multiple training courses throughout the year, including: HPC 101, HPC for Python and R, and advanced courses in parallel programming with MPI. We also provide support and project consultations to help you take your science to the next level through the use of HPC.

Research
What new technologies and scientific computing tools are available?

We research new HPC technologies and promising scientific computing tools in order to provide scientists with access to the most beneficial types of equipment, software, and computational methodologies to improve scientific workflows and eliminate computational bottlenecks.

Partnerships
What if I need access to larger computational resources?

We have partnerships with the NSF XSEDE program, DOE Oak Ridge National Laboratory, and the Rocky Mountain Advanced Computing Consortium (RMACC) and can help you prepare and write proposals to apply for allocations on larger machines.

For more information or help getting started, contact us at hpc@usgs.gov.

Publications

Year Published: 2018

Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed

The U.S. Geological Survey monthly water balance model (MWBM) was enhanced with the capability to simulate glaciers in order to make it more suitable for simulating cold region hydrology. The new model, MWBMglacier, is demonstrated in the heavily glacierized and ecologically important Copper River watershed in Southcentral Alaska. Simulated water...

Valentin, Melissa M.; Viger, Roland J.; Van Beusekom, Ashley E.; Hay, Lauren E.; Hogue, Terri S.; Foks, Nathan Leon
Valentin, M.M., Viger, R.J., Van Beusekom, A. E., Hay, L.E., and Hogue, T. S., (submitted), Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed. Journal of Geophysical Research - Earth Surfaces. doi: 10.1029/2017JF004482

Year Published: 2018

Common hydraulic fracturing fluid additives alter the structure and function of anaerobic microbial communities

The development of unconventional oil and gas (UOG) resources results in the production of large volumes of wastewater containing a complex mixture of hydraulic fracturing chemical additives and components from the formation. The release of these wastewaters into the environment poses potential risks that are poorly understood. Microbial...

Mumford, Adam C.; Akob, Denise M.; Klinges, J. Grace; Cozzarelli, Isabelle M.
Mumford AC, Akob DM, Klinges JG, Cozzarelli IM. 2018. Common hydraulic fracturing fluid additives alter the structure and function of anaerobic microbial communities. Appl Environ Microbiol 84:e02729-17

Year Published: 2018

Demography of the Pacific walrus (Odobenus rosmarus divergens) in a changing Arctic

The Pacific walrus (Odobenus rosmarus divergens) is a candidate to be listed as an endangered species under United States law, in part, because of climate change‐related concerns. While the population was known to be declining in the 1980s and 1990s, its recent status has not been determined. We developed Bayesian models of walrus population...

Taylor, Rebecca L.; Udevitz, Mark S.; Jay, Chadwick V.; Citta, John J.; Quakenbush, Lori T.; Lemons, Patrick R.; Snyder, Jonathan A.
Taylor, R.L., and Udevitz, M.S. 2015. Marine Mammal Science, vol. 31, no. 1, p. 231-254. doi: 10.1111/mms.12156