Systems Overview
Below is a representative sample of the types of systems that we manage and support. Some are owned by individual PIs or groups, for the use of their research teams; others are truly campus-wide offerings. If you are interested in having Research Computing manage or host your servers, or if you are interested in investing in new equipment for the Sherlock cluster, please contact us at srcc-support@stanford.edu.
Systems information is also available on the Compute Clusters and HPC Platforms and Getting Started on our HPC Systems pages.
Sherlock
Sherlock is a shared compute cluster available for use by all Stanford faculty and their research teams for sponsored or departmental faculty research. Research teams have access to a base set of general compute nodes, GPU-based servers, and a multi-petabyte Lustre parallel file system for temporary files. Faculty can supplement these shared nodes by purchasing additional servers, thus becoming Sherlock owners. Sherlock supports more than 6,300 users from over 1000 research groups at Stanford.
Oak Storage | Elm Cold Archive
Oak is an affordable storage tier for active research data featuring tight integration to the Sherlock and SCG clusters, designed for data with a long working lifetime that nonetheless needs to remain readily accessible. Elm provides cold storage of large research datasets — from terabytes to hundreds of petabytes — addressing the need for inexpensive, dependable preservation of research data for future use, or to meet regulatory and policy compliance obligations.
FarmShare
FarmShare is a compute environment available to students, faculty and staff with fully sponsored SUNetIDs. FarmShare is a great resource for coursework-related and miscellaneous computational tasks. This resource also helps those doing research have a place to try codes and learn about technical solutions to assist in reaching their research goals, prior to scaling up to Sherlock or another cluster.
Nero GCP
Nero GCP is a shared secure computing platform specifically designed for High Risk Data. This computing environment leverages the public cloud to address the significant demand for a secured computational environment to work with PHI and other high risk data at scale.
Nero is available to any team led by a researcher with Principal Investigator privileges at Stanford (e.g.: faculty, or researchers with a PI waiver) working exclusively with high-risk data.
Carina Computing Platform
Carina is a secure computing environment, designed and approved for PHI and other High-Risk Data.
Carina is available to any team led by a researcher with Principal Investigator privileges at Stanford (e.g.: faculty, or researchers with a PI waiver) working exclusively with high-risk data.
The Stanford Genomics Cluster
The Stanford Genomics Cluster is available to members of the Genomics research community at Stanford. With scientific support from the Genetics Bioinformatics Service Center (GBSC) and system administration from Stanford Research Computing, the SCG cluster uses a charge-back model to provide access to a wide array of bioinformatics tools, techniques and scientific support expertise. It has approximately 4600 compute cores and 9PB of usable storage.
Marlowe GPU-Based Computational Instrument
Marlowe is an NVIDIA DGX H100 Superpod, built using NVIDIA’s reference architecture and designed to deliver cutting-edge computational performance. This newest Stanford HPC cluster is managed by Stanford Data Science, with hardware and software infrastructure administered by Stanford Research Computing.
NSF Access: Discover nationwide NSF cyberinfrastructure
Need advanced computing and storage options for your research or classroom? The ACCESS program has been established and funded by the U.S. National Science Foundation to help you, the nation’s researchers and educators - to use some of the country’s most advanced computing systems and services — at no cost to you.
With more than 30 resources from more than 15 resource providers, there’s bound to be a resource for you, your lab, or your class.
Quantum Learning Machine
The Atos Quantum Learning Machine is a complete appliance that offers a universal programming environment to avoid vendor lock-in. The Atos Quantum Assembly Language, a hybrid language based on Python, allows programmers to develop their own algorithms on any existing or future quantum programming framework.