Hardware Resource
AI Cluster (Supercomputing)
The AI cluster adopts the framework of K8S+Docker, supporting completely independent environment for single user. The cluster can help users easily upload private images or import images from external container libraries in docker community for development and training. The cluster helps users greatly reduce costs of learning through a friendly graphical interface, quickly complete the deployment of the computing environment and start their scientific research calculations. Currently, the AI cluster contains 25 computing nodes, a total of 1584 cores in the CPU, and a total of 146 GPU cards.
CE Cluster (Artificial Intelligence Computing)
This cluster uses HPC cluster management software to manage software and hardware resources, and rationally schedules jobs submitted by users according to the resource usage, so as to improve resource utilization and job execution efficiency. Currently, it is equipped with 29 computing nodes, 1344 CPU cores, 38 GPU cards, and 8 DCU cards.
OD Cluster (Artificial Intelligence Computing)
The OD cluster is similar to the CE cluster in architecture and usage, but its computing nodes have a graphical interface and can directly connect to the Internet, which is a good complement to the CE cluster in function. At present, the OD cluster has 27 computing nodes, 782 CPU cores, and 18 GPUs.
Storage
The parallel file system makes all computing nodes in the cluster capable of reading and writing files in the storage system through the same file directory. Furthermore, it can accommodate the large-scale random IO, frequent read and write operations, and massive communication loads. The high-Performance computing platform adopts a stable commercial version of parallel storage, with an available capacity of 2PB.
Computing network
The platform adopts the IB HDR 200G network architecture, with a bandwidth of 100G.
Software Resources
| CE Cluster Software Resources | |||
|
Categories |
Name |
Available Versions |
Synopsis |
|
Compiler |
GNU Compiler |
4.8.5 7.5 8.3 10.02 |
GNU compiler |
|
Intel Compiler |
2018u1 2020u1 |
Intel compiler |
|
|
intel oneapi |
2021.3.0 |
Intel oneapi compiler |
|
|
CUDA |
10.0 11.0 11.4 |
Cuda compiler |
|
|
Programming Language |
Julia |
1.6.2 |
A high-level, high-performance, dynamic programming language |
|
go |
1.15.3 |
A statically typed, compiled programming language |
|
|
matlab |
2021a |
A proprietary multi-paradigm programming language and numeric computing environment |
|
|
python |
3.6.7 3.7.3 3.9.6 |
An interpreted high-level general-purpose programming language |
|
|
R |
4.1 |
A programming language and free software environment for statistical computing and graphics |
|
|
Software for Data Science |
stata |
16 |
A general-purpose statistical software package for data manipulation, visualization, statistics, and automated reporting |
|
Software for Material Science |
cp2k |
8.2 |
A freely available quantum chemistry and solid state physics program package |
|
Software Environmental Management |
anaconda |
2019.10 2021.5 |
A distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment |
|
singularity |
3.5.2 |
Used for operating system level virtualization, also known as containerization |
|
| AI Cluster Mirror Resources | |
|
PyTorch |
An open source machine learning library based on the Torch library |
|
TensorFlow |
A free and open-source software library for machine learning and artificial intelligence |
|
Caffe |
A deep learning framework made with expression, speed, and modularity in mind |
|
MxNet |
A deep learning software framework, used to train, and deploy deep neural networks |
|
PaddlePaddle |
An easy-to-use, efficient, flexible and scalable deep learning platform |