By Yeo Eng Hee, Research Computing, NUS Information Technology, on 20 January 2020
A recent paper in March 2019[i] indicated that the number of HPC sites worldwide that run some workloads in the Cloud has increased in proportion from 13% in 2011 to 74% in 2018. Cloud service providers recognise this trend. They are making it easier and more attractive for HPC workloads to run in their clouds.
By Liang Senwei, Department of Mathematics, Faculty of Science, on 20 January 2020
Machine learning has become an important area of research in scientific computing. In particular, deep neural networks with powerful universal approximation and generalisation capacity get more and more attention and they are applied in many applications, such as image classification, natural language processing. I am currently engaged in deep learning on image classification and on mathematics problems. This article mainly introduces my works on improving the generalisation of deep networks for image classification.
By Wang Junhong, Research Computing, NUS Information Technology, on 20 January 2020
Artificial Intelligence (AI) and deep learning became one of the hottest topics not only in the industrial and real life application development but also in the research domain. In NUS, researchers from different departments are working on research projects such as stereo matching, quantum many-body systems, computer vision, image classification, natural language processing and others by applying the AI and deep learning methodology. In this article, it is our pleasure to have three very active researchers sharing their on-going deep learning research works. Every researcher is also welcomed to share your research work and experience in using HPC/GPU resources.
Combining R and Python using Reticulate
By Vamshidhar Gangu, Research Computing, NUS Information Technology, on 20 January 2020
While starting a data science project, one of the important decisions to make is choosing what programming language or libraries to use? And the two programming languages that might immediately come to mind are R and Python. Tough both of them are excellent tools in their own right, they are often conceived as rivals instead of options.
By M Ganeshkumar, PhD Student, NUS Graduate School of Science and Engineering (NGS), Department of Electrical & Computer Engineering (ECE), on 20 January 2020
GPUs/TPUs are used to increase the processing speed when training deep learning models due to its parallel processing capability. Reinforcement learning on the other hand is predominantly CPU intensive due to the sequential interaction between the agent and environment. With the recent popularity of deep reinforcement learning (deep RL) algorithms, understanding how to shorten processing speed based on the available resources becomes imperative. The purpose of this report is to highlight some considerations to optimise the computational resources available for deep RL, specifically the size of network used.
By Kumar Sambhav, HPC Specialist, NUS Information Technology, on 20 January 2020
Research Computing at NUS IT provides a Hadoop based Data Repository and Analytics service. This system can be used to host your big data in the Data Repository and to analyse the data using Analytics Service. The Analytics Service part of DRAS provides services from real-time analytics to running machine learning using Spark.
By Wang Junhong, Research Computing, NUS IT on 21 Oct, 2019
When you start to work on a high-performance computing (HPC) cluster to submit and run your computational jobs, you may want to know the available job queues for job submission and the load status in these queues in order to select an appreciate queue. After jobs are submitted and running in the queues, you may also want to know the computing speed and parallel computing performance of the jobs. Your program may generate a lot of data sets or result files which occupy large amount of storage space, so you may want to know the available storage space allocated to you and the usage.
By Choong Wey Yeh, Lucas on 21 Oct, 2019
The High Performance Computing (HPC) team at NUS IT provides services and resources for users to run large-scale computational jobs. These jobs range from machine learning programs to simulation programs by users from various departments. However, users may not always know the right amount of computational resources to request for their jobs, often resulting in resources requested being underutilised. With more resources requested than utilised, more unused resources are hogged on the HPC clusters than necessary which results in longer queueing times for other users waiting for their turn to run jobs.
By Ku Wee Kiat, Research Computing, NUS IT on 21 Oct, 2019
Tensorflow provides pre-built and pre-trained models in the Tensorflow Models repository for the public to use.
The official models are a collection of example models that use TensorFlow’s high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimised for fast performance while still being easy to read.
By Yeo Eng Hee, Research Computing, NUS IT on 21 Oct, 2019
In our previous newsletter, we described how some of our HPC clusters in the shared resource pool are now running with compute nodes from the cloud. This was made possible with the implementation of a virtual private network between NUS and AWS, our cloud solution provider, extending our HPC into the cloud in a seamless manner. Job scheduling is still managed by PBS Pro, and the usual queuing is still necessary to ensure that jobs are scheduled in the cloud compute nodes in a fair manner.
