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current students

The Student HPC Platform has been enhanced to provide students with a more accessible and user-friendly computing environment. Starting from 1st August, 2023, users will have access to a new HPC Platform that includes an additional web environment. 

While users can still use the traditional SSH access method, the new web-based interface offers a seamless and intuitive experience for developing and running applications. Built-in applications with Web GUI interfaces, such as Jupyter Notebook/JupyterLab and RStudio, make it easy for students to develop their applications via web browser. 

The new platform also offers easy-to-use interfaces for accessing the HPC platform via web browser, including traditional SSH access and a GUI-based desktop. This provides students with the performance of CPU and GPU resources for applications in data visualization, simulations, data analytics, modelling, and more. 

In addition, the new web portal offers easy file management capabilities, allowing users to upload, download, and manage their files and data quickly and efficiently without additional tools except for their web browser. The desktop experience is designed to be familiar to students, enabling them to work in an environment that they are comfortable with. 

To get started, simply click here to create your account without any fees. Once you have created your account, please visit here to start exploring the new platform. We look forward to seeing you there! 

Hardware and Software Resource

(Effective from 1 Aug 2023)

Hardware

Total No of CPU Cores

Over 100 cores

Total Memory Size

Over 2TB

GPU Cards 

4 x NVIDIA RTX 3080 Ti

*We are upgrading the CPU and GPU resources on the Student HPC Platform, which we aim to complete by 2023.

Software

OpenHPC Stack

Category

Component

Base OS

Rocky Linux Release 8.6 x86_64

Compilers

GNU9.4.0 (gcc, g++, gfortran)

GNU12.2.0 (gcc, g++, gfortran)

MPI libraries

OpenMPI, MPICH

Software provisioner

Lmod

Resource manager

SLURM, Munge

Serial/Threaded/Parallel Libraries

Boost, FFTW, Hypre, PETSc, Scalapack, LAPACK, OpenBLAS, SuperLU, Trilinos

IO libraries

HDF5, NetCDF, Adios

Development tools

Autoconf, Automake, Libtool, Valgrind

Debugging and profiling tools

TAU, Likwid, Dimemas

Installed Applicatons

Application

Versions

Anaconda3

Anaconda3/2023.03-1

Apptainer 1.1.8
CUDA 12.1
FSL 6.0.6.5
Go 1.20.4
Gromacs

2023.1

2023.1-GPU

LAMMPS

28Mar2023

28Mar2023-GPU

NVHPC 23.5
Plumed 2.9.0
Quantum Espresso 7.2
Singularity 3.11.3

 

 

Job Queue Configuration

 

 

Queue name

 

No. of nodes

No. of CPU cores 

per node

Usable Memory (GB) 

per node

 

No. of GPU cards

 

Status

h07q1

10

24

500

N/A

4 nodes in service,

others will be available

by end of October

h07gpuq1

1

24

256

4

In service

h07q2 8 36 500 N/A

4 nodes in service,

others will be available

by end of September

  

 

Resource Limits

User Disk Quota: 70GB (18GB for pre-installed Python packages) 

 

For General Job Submission:

 

Queue name

Max no. of CPU Cores

per job

Max concurrent job

able to be run per user  

Max concurrent job

able to be submitted per user

No. of GPU Cards

per job  

Maximum run time

limit for jobs (hrs)

h07q1

24

2

3

N/A

72

h07gpuq1

6

1

2

1

72

 

Jupyter Notebook/Lab App:

 

Queue name

Max no. of CPU Cores

per job

Max no. of Memory (GB)

per job

Maximum runtime/

walltime (hours)

Minimum CPU cores 

per job

Minimum memory (GB)

per job

h07q1

8

128

48

2

4

h07gpuq1

6

64

36

2

4

 

RStudio App:

 

Queue name

Max no. of CPU Cores

per job

Max no. of Memory (GB)

per job

Maximum runtime/

walltime (hours)

Minimum CPU cores 

per job

Minimum memory (GB)

per job

h07q1

8

128

48

2

4

h07gpuq1

6

64

36

2

4

 

Desktop App:

 

Queue name

Max no. of CPU Cores

per job

Max no. of Memory (GB)

per job

Maximum runtime/

walltime (hours)

Minimum CPU cores 

per job

Minimum memory (GB)

per job

h07q1

8

128

36

2

4

h07gpuq1

6

64

36

2

4