NSCC ASPIRE Issue #2 – NTU Student Team Bags Top Honours at Two International HPC Student Cluster Competitions.

Team NTU clinching the Overall Winner award for the Student Cluster Competition at SC17 at Denver, Colorado.
NTU team with Baidu staff (1st row, 2nd from left): Wu Bian (C​S Year 1), Li Yuanrui (CS Year 4), Assoc Prof Francis Lee. (2nd row, 3rd from left) Liu Siyuan (CS Year 3), Shao Yiyang (CS Year 3), Ren Daxuan (CS Year 3), Tan Ying Hao (CS Year 2). (3rd row) Lu Shengliang (CE Class of 2016).​

ISC 17, Frankfurt, Germany, 21 June – Alongside formidable teams from Tsinghua University and Beihang University, beating eight other teams from various international universities, Team from Nanyang Technological University (NTU), sponsored by NSCC and led by Associate Professor Francis Lee Bu Sung, won the Deep Learning Challenge Award for solving the Captcha Challenge and achieving the highest degree of model accuracy at ISC 2017’s student cluster competition, now into its sixth edition. This special award was sponsored by Baidu Cloud.

There were a total of eleven teams from around the world participating this year in Frankfurt, to build a small cluster of their own design to compete in the competition and to test their High Performance Computing (HPC) skills by optimising and running a series of benchmarks and applications.

SC 17, Denver, United States, 17 Nov –  The NSCC-sponsored student team from NTU shattered two benchmark records with their cluster, posting a SCC LINPACK score of 51.77 TFlop/s, beating the previous record of 37.05 TFlop/s, held by Germany’s Friedrich-Alexander-Universitat (FAU).

The team then went on to capture the competition’s HPCG record — a benchmark meant to mimic modern HPC workloads — with a score of 2,056, easily topping the 1,394 record set by the Purdue/NEU team six months ago at ISC’17.

With the two record-breaking wins, it is little surprise that Team NTU was conferred the honour of SC17 Overall Winner, out of a total of 16 teams, hailing from China, Germany, Poland, Singapore, the United States and Taiwan.

“I was a little surprised we won,” admitted modest Nanyang Technical University team co-leader Liu Siyuan, whose team was considered a long shot by industry experts.

“We are very excited to finish ahead of such strong teams,” the other co-leader Shao Yiyang added, who also said they knew the team to beat was China’s Tsinghua University student team. Tsinghua was the favoured team, having won two previous international student cluster competitions in 2017, at ASC17 in Wuxi, China, as well as ISC17 in Frankfurt, Germany.

Congratulations to Team NTU on their wins!

NSCC ASPIRE Issue #2 – SCA2018 – Gathering the Best of HPC in Asia

SCA2018 - Gathering the Best of HPC in Asia

NSCC is embarking on SupercomputingAsia 2018 (SCA18), an inaugural annual conference that will encompass an umbrella of notable supercomputing and allied events in Asia with the key objective of promoting a vibrant and shared HPC ecosystem in Asia, where the most exciting HPC developments are taking place.

SCA18 will be held from 26 to 29 March 2018 at Resorts World Convention Centre, Singapore.

The scientific programme of SCA18 has its roots in Supercomputing Frontiers (SCF), which is Singapore’s annual international HPC conference which provides a platform for thought leaders from both academia and industry to interact and discuss visionary ideas, important global trends and substantial innovations in supercomputing. The conference was inaugurated in 2015 and helmed by A*STAR Computational Resource Centre (A*CRC). In March last year, the National Supercomputing Centre (NSCC) Singapore took over hosting of Supercomputing Frontiers 2017 (SCF17). 

SCF17 was attended by over 450 delegates from over 12 different countries. Riding on the success of the Supercomputing Frontiers conference series, SCA18 programme highlights will include: HPC Technology Updates & Case Studies, Scientific paper presentations, Academic activities & workshop for students and Co-located HPC events.

Co-located events include:

Asia-Pacific Advanced Network Meeting (APAN45)

Conference on Next Generation Arithmetic (CoNGA)

Singapore-Japan Joint Sessions

Supercomputing Frontiers Asia (SCF Asia)

Towards an Asia Pacific Research Platform (APRP)

To find out more, visit SCA18 website here or follow us on Facebook here

Lecture Session Invitation: Machine Learning and AI in Biomedicine

Lecture Session Invitation:
Machine Learning and AI in Biomedicine

(Due to overwhelming responses, registration for the session is now closed.)

Professor Vladimir Brusic, Griffith University, Queensland, Australia


Date: 22 December 2017
Time: 6.00 pm – 7.00 pm
Location: Charles Babbage Room,
National Supercomputer Centre, Singapore, 1 Fusionopolis Way, #17-01 Connexis South


The World’s total data is currently doubling every two years. This data expansion includes not only the
growth in quantity, but also in complexity and the types of data. The enormous rate of generation and online
access to data is profoundly changing the way how health business and biomedical research is
conducted. Biomedical data include R&D data, clinical data, activity and cost data, patient behavior data,
basic science data, and standards and ontologies, among others. Big Data approaches are increasingly
needed for utilization of results from various Omics studies and their translation into clinical practice.
These applications include predictive and content analytics for a variety of applications that support drug
discovery and optimization, the development of new diagnostic methods, and personalization of
medicine. Biomedical data vary in granularity, quality, dimensionality and complexity. There is a variety
of sources and data formats – web pages, publications, technical reports, images and graphs, tables, and
databases. The challenge is to make the transition from data to actionable knowledge. An emerging area
is the use of knowledge-based approaches for Big Data analytics whereby well-annotated data are
combined with specialized analytical tools and integrated into analytical workflows. A set of well-defined
workflow types with rich summarization and visualization capacity facilitates the transformation from
data to critical information and knowledge that enable understanding, decision making, and selection of
actions for solving various problems. Statistical and artificial intelligence methods are used as analytical
engines to make sense of rich datasets. The emerging Big Data requires dynamic integration of
standardized data into knowledge bases to make selected data sources accessible through integration
with the analytical tools. We will demonstrate the utilization of Big Data Analytics, mathematical
modeling, and artificial intelligence tools as well as challenges with two distinct examples: diagnosis of
endometriosis, and design of universal multivalent vaccines.

Biographical sketch

Dr. Brusic is a Professor at Menzies Health Institute Queensland, Griffith University, Australia. He is an
Adjunct Professor of Computer Science at the Metropolitan College, Boston University, USA and a Visiting
Professor at Kumamoto University, Japan. Dr Brusic holds BEng (Mechanical Engineering), MEng
(Biomedical Engineering), MAppSci (Info Tech), MBA, and PhD degrees. He holds a Honorary Doctorate in
Medicine from Semmelweis Medical University, Budapest, Hungary.
His previous positions include faculty or PI appointments at Harvard Medical School (Boston, USA),
University of Queensland (Brisbane, Australia), National University of Singapore, Nanyang Technological
University (Singapore), Institute for Infocomm Research (Singapore), and Walter and Eliza Hall Institute
for Medical Research (Melbourne, Australia). Dr Brusic is an Associate Editor of Frontiers in Immmunology
and an Editor of Briefings in Bioinformatics. He has published more than 200 scientific publications and
two patents. His work has attracted 12,000 citations and h-index of 47. The list of publications can be
found here:
His work is in the fields of Bioinformatics and Medical Informatics in immunology, cancer, medical
diagnostics, and modeling of biological systems. His research interests span the fields of Big Data analytics,
Biological Databases, computational modeling of biological systems, simulation of molecular interactions,
and biological discovery using simulation of laboratory experiments. He has developed knowledge-based
systems for biological data mining and knowledge discovery. Recently, Dr. Brusic has been developing a
new field of elemental metabolomics that focuses on the study of elements in biological and
environmental samples, transfer of elements along the food chain and environmental exposure, and their
effects on human health.


Jointly organized with: