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A. UBDA Computing Intensive Service


The UBDA platform provides researchers to run and execute their own programs and applications in multiple CPU cores, nodes and GPU environment with the parallel file system support. The job queue scheduling system is used for this platform for a user to run his/her programs/applications. A user requires to submit a job into different job queues for different resource requirements, like the maximum no. of computing nodes, GPUs, maximum no. of CPU cores in a single computing node, etc. 


To run a code on the UBDA platform, the following job queue can be selected as per your application requirements:

Job Queues' Configuration

Job queue Max no. of nodes No. of CPU cores per node No. of GPU card per node Useable memory per node (GB)
q2s01 25 26 N/A 100
q4s01 3 64 N/A 1000
qgpu01 2 28 1 100
1 32 1 100
6 32 2 100
2 32 4 100
qmic01 2 68 N/A 120

Job Queues' Resource Limit

Job queue Max no. of CPU core for a job Max no. concurrent job per user Max no. of job(s) able to be submitted concurrently per user Maximum run time limit (Walltime) for jobs (hrs)
q2s01 104 1 3 72
q4s01 104 1 3 72
qgpu01 104 1 3 72
qmic01 104 1 3 72

User Storage Quota

200 GB 


For the different demands from different projects, a user should let us know what computing resources they need, like no. of CPU cores, GPU, storage size as well as the applications to setup on the UBDA platform. We are pleased to work with you to setup and build for running your desired programs/application. For such requirements, users should let us know their plans to reserve the resources.


B. UBDA Big Data Service


  1. Virtual machine

    This service provides researchers to have their own virtual machine(s) (VM) to run and test their applications needed with highly control with operation system. Users could install and highly customerize their application in this VM environment.

  2. Online JupyterLab Development Tool with GPU

    This is JupyterLab online services supporting GPU cards. User could use this GPU-enabled JupyterLab environment to develop and run the research application supporting GPU like Tensorflow and Keras.