Rikky Purbojati, Research Computing, NUS Information Technology, on 24 May 2022
Quantum computing is undeniably one of the most hyped technologies in recent years. With seemingly frequent advancements and milestone announcements, this field has attracted billions of investments into both established and nascent quantum computing vendors. The penultimate promise of quantum computers is that they will enable the computation of intractable problems that classic computers cannot easily solve.
Ku Wee Kiat, Research Computing, NUS Information Technology, on 11 May 2022
In this article, we will cover how to train a segmentation model using Detectron2 on your COCO-annotated dataset on NUS HPC GPU Cluster.
Wang Junhong, Research Computing, NUS Information Technology, on 6 June 2022
Artificial Intelligence (AI) adoption and Machine Learning (ML) have become hot topics and trends in many domains. In NUS, many researchers and students are carrying out AI training for various research projects. In this article, three interesting AI training projects carried out in the central AI/GPU platform will be shared by the statistics nature of the performance of the AI training and the researchers’ valuable feedback. The three researchers are selected from the list of top AI/GPU users in Q1 of 2022.
Xie Weihang, Canepa Research Group, Department of Materials Science and Engineering, College of Design and Engineering, on 29 April 2022
The Vienna Ab initio Simulation Package (VASP) is a well-known and largely used software package for performing DFT calculations. VASP requires a significant amount of computing resources and was ranked top 5 used applications on NSCC (National Supercomputing Centre) in terms of CPU hours. Starting from version 6.2.0, VASP is officially capable of acceleration by GPU through the OpenACC port.
Pranab Kumar Das, Senior Research Fellow, Singapore Synchrotron Light Source, on 9 May 2022
I am a researcher at the Singapore Synchrotron Light Source of NUS, primarily working on experimental projects where we study the electronic band structure of various novel and functional materials by employing Spin- and Angle-Resolved Photoemission Spectroscopy (Spin-ARPES).
Kuang Hao, Research Computing, NUS Information Technology, on 17 May 2022
Anomaly detection is widely used in behavioural analysis to determine the types of anomalies occurring in a given data set. In domains such as fraud detection, intrusion detection, fault detection and system health monitoring, anomaly detection helps to avoid system damages and potential financial breaches.
Following up on our last article posted in 2020, we will introduce an additional complex machine learning algorithm in anomaly detection and demonstrate how we can do that on our HPC cluster in this article.
