The Scientific Compute Platform provides WashU research faculty access to computing resources and a job scheduler that runs large-scale, parallel computing tasks with access to many CPU and GPU cores, large amounts of RAM, high-speed networks, and high-performance storage systems.

The service is centered around container technologies (e.g. Docker) to allow complex software environments to be deployed independently from other users while isolating complicated software dependencies. The Scientific Compute Platform aims to be well integrated with the Data Storage Platform, the Research Applications Platform, and Cloud Services, providing an ability to expand computational resources to integrated cloud solutions.

**Billing for Compute services will be waived until FY22 to give researchers time to use the service and budget for usage.

Access

Please review the four sections below to see if research computing is a good fit for your needs. 

Getting Started

The following are the steps required to activate access to the compute service. Collect the following information and select a compute resource:

  • Faculty WUSTL Key ID
  • WUSTL Key IDs for the members of your lab or research group
  • Department Number – 6 digit department number
  • Network Location – The network IP address, which you can get by using this site: https://speedtest.ris.wustl.edu/
  • Technical Contact – The WUSTL Key ID of a member of your lab RIS can contact regarding technical aspects of the service
  • Billing Contact – The WUSTL Key ID of a member of your lab RIS can contact regarding billing
  • Any storage service allocations you intend to integrate with the compute service

Intended Users

Compute services are available to all WashU faculty, staff, or students involved in research.

Requirements and Considerations

Before accessing computing services, the following requirements must be met:

  • Must have a billable department at WashU
  • Should have an understanding of Linux computing environments
  • Have an understanding of container technologies like Docker

General Features

  • Base system
    • 5,000 Intel Cascade Lake Cores
    • 120 Nvidia Telsa V100 GPUs
    • 300TB DDN high-performance scratch space
    • 100Gbit Mellanox HDR Network
  • Batch computing across thousands of CPU and hundreds of GPUs
  • Integration with WashU Research Network (WURN) (40Gbit)
  • Integration with Data Transfer
  • Integration with Research Storage
  • Independent software run times with Docker
  • Integration with WUSTL Key Identity
  • Dedicated 10Gbit connection to Google Cloud
  • Free training seminars and webinars

Options

Compute Resources

Compute Condo