The Horizon 2020 Future and Emerging Technologies programme: Dynamical Exascale Entry Platform - Extreme Scale Technologies (DEEP-EST) project has finished its work and was praised for its results in the final review of the project's outcome.

We still have to harvest our results by writing publications on the results, but you can find a video already here:

All our travel emissions have been offset. As it is not clear whether funding regulations allow to offset emissions due to supercomputer energy consumption, these were not compensated. However, one of the research topics of the DEEP-EST project was energy efficiency and we achieved a lot by using specialised (=more efficient) accelerator hardware.

The European Centre of Excellence RAISE (Research on AI- and Simulation-Based Engineering at Exascale) is holding an online seminar on using the Interaction Room Software Engineering approach for HPC Systems Engineering.

This approach has been described in this publication:
Matthias Book, Morris Riedel, Helmut Neukirchen, Markus Götz.
Facilitating Collaboration in High Performance Computing Projects with an Interaction Room.
The 4th ACM SIGPLAN International Workshop on Software Engineering for Parallel Systems (SEPS 2017). Co-located with SPLASH 2017 as an ACM SIGPLAN-approved workshop.
October 23, 2017, Vancouver, Canada. DOI: 10.1145/3141865.3142467, ACM Digital Library 2017.
Download

The recording of the online seminar can now be found on the CoE RAISE YouTube channel:

The University of Iceland's Computer Science department is researching machine learning using the next generation's supercomputer DEEP-EST -- by the way: we are also offering a Data Science specialisation in our Computer Science programme, where, e.g., machine learning including deep neural networks is covered. To showcase what is possible if you have a supercomputer to train neural networks, we offer a web page that allows you to use the camera of your smartphone (or laptop) to detect objects in real-time.

Just open the following web page and allow your browser to use the camera: https://nvndr.csb.app/
(Allow up to approx. 1 minute for loading the trained neural network and for initialisation. Web page works best in landscape orientation.)

While neural networks are still best trained on a supercomputer, such as DEEP-EST with its Data Analysis Module, the trained neural network even runs in the browser of a smartphone (purely running locally as Javascript in your browser without any connection to a supercomputer, i.e. completely offline after having downloaded the Javascript code and the trained neural network).

The used approach is Single Shot Detector (SSD) (the percentage shows how sure the neural network is about the classification) using the MobileNet neural network architecture. The dataset used for training is COCO (Common Objects in Context), i.e. only objects of the labeled object classes contained in COCO will get detected. The Javascript code that is running in your browser uses Tensorflow Lite and its Object Detection API.

Example object detection via a neural network

If you want learn more about the DEEP-EST project where the next generation supercomputer is developed, have a look at the poster below (click on the picture below for PDF version):

University of Iceland is part of the European Centre of Excellence RAISE (Research on AI- and Simulation-Based Engineering at Exascale) that has started in 1/2021 and will end 6/2024. It is funded by the European Commission's Horizon 2020 programme with an overall budget of € 4 969 347. The University of Iceland's team is lead by Morris Riedel together with Matthias Book and Helmut Neukirchen (all professors at the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science) and several PhD students are funded by this project.

Compute- and data-driven research encompasses a broad spectrum of disciplines and is the key to Europe’s global success in various scientific and economic fields. The massive amount of data produced by such technologies demands novel methods to post-process, analyze, and to reveal valuable mechanisms. The development of artificial intelligence (AI) methods is rapidly proceeding and they are progressively applied to many stages of workflows to solve complex problems. Analyzing and processing big data require high computational power and scalable AI solutions. Therefore, it becomes mandatory to develop entirely new workflows from current applications that efficiently run on future high-performance computing architectures at Exascale. The RAISE Center of Excellence for Research on AI- and Simulation-Based Engineering at Exascale will be the excellent enabler for the advancement of such technologies in Europe on industrial and academic levels, and a driver for novel intertwined AI and HPC methods. These technologies will be advanced along representative use-cases, covering a wide spectrum of academic and industrial applications, e.g., coming from wind energy harvesting, wetting hydrodynamics, manufacturing, physics, turbomachinery, and aerospace. It aims at closing the gap in full loops using forward simulation models and AI-based inverse inference models, in conjunction with statistical methods to learn from current and historical data. In this context, novel hardware technologies, i.e., Modular Supercomputing Architectures, Quantum Annealing, and prototypes from the DEEP project series will be used for exploring unseen performance in data processing. Best practices, support, and education for industry, SMEs, academia, and HPC centers on Tier-2 level and below will be developed and provided in RAISE's European network attracting new user communities. This goes along with the development of a business providing new services to various user communities.

I am giving a talk on the H2020 projects DEEP-EST and EOSC-Nordic (including EUDAT's B2FIND and B2SHARE) at the University of Iceland's Engineering Research Institute seminar: From Exascale Supercomputing to FAIR data -- or: Why (almost) everyone uses GPUs and how to get a DOI for your dataset.

For zoom link, have a look at the official announcement.

The slide are available via DOI:10.23728/b2share.a6a4682fe1f74b32b8b67948f7ce6965
and a video recording of the presentation is available as well.

Update: The Top 500 list of the fastest supercomputer shows that the Flop/s growth is since 2013 not exponentially anymore, but is already slowing down. While Moore's low was about the number of transistors of cost optimised systems and not about Floating Point Operations per Second of Supercomputing systems, both are nevertheless related to each other and show that Moore's law is slowly coming to an end.

Our new colleague Morris Riedel gave on 7. February 2020 a presentation on Quantum Computing (slides / video) at UTmessan 2020, Icelands biggest IT fair. In addition, the Computer Science Department ran on the public visitor day (8. February 2020) a booth: beside student projects, we showcased research projects, e.g. DEEP-EST.

Morris Riedel talking about Quantum Computing

The booth of the Computer Science department, showcasing student projects.

The booth of the Computer Science department, showcasing research projects.

The DEEP-EST project

Showcasing the DEEP-EST project

For showcasing the machine learning that we do in the DEEP-EST project, we offer a web page that allows you to use the camera of your smartphone (or laptop) to detect objects in real-time. While neural networks are still best trained on a supercomputer, such as DEEP-EST with its Data Analysis Module, the trained neural network even runs in the browser of a smartphone.

Just open the following web page and allow your browser to use the camera: https://nvndr.csb.app/.

(Allow a few seconds for loading the trained model and initialisation.)

The used approach is Single Shot Detector (SSD) (the percentage shows how sure the neural network is about the classification) using the MobileNet neural network architecture. The dataset used for training is COCO (Common Objects in Context), i.e. only objects of the labeled object classes contained in COCO will get detected. The Javascript code that is running in your browser uses Tensorflow Lite and its Object Detection API and model zoo.

Example object detection via neural network

If you want learn more about DEEP-EST, have a look at the poster below (click on the picture below for PDF version):

Many academics insist on that journals are better than conferences, e.g. some PhD programmes have unwritten rules that a PhD thesis needs to involve at least one journal publication (which can be really a problem, because some journals have 1.5 year time span from submission to publication; add this to another 1.5 year for a PhD student to produce the first results being worth published in a journal/top conference, then this is almost impossible in 3 years of PhD study).

For Computer Science, some conference are as hard (or even harder) as journals, e.g. in terms of acceptance rates (which however depends also a lot, e.g. having a lot of crap submissions automatically leads to a low acceptance rate). Also Computer Science is a very fast developing field, so results would be often outdated after 1.5 years, so the far shorter publication cycles make conferences far more attractive.

As an example, below are two rankings (based on impact, i.e. citations such as h-index) that show that Computer Science conference are as high-quality (or even higher) as journals. Of course, you can always find conferences (but also journals) that have a low impact: therefore, instead of claiming that in general journals are better than conferences, you always need to look at each particular conference, but also at each particular journal (acceptance rates are missing in these lists -- they would be nice to compare, but this data is tedious to collect):

• HPC and Supercomputing Conference and Journal Rankings
• The top journals and conferences in data mining / data science

Shahbaz Memon successfully defended his PhD thesis in Computer Science on Standards-based Models and Architectures to Automate Scalable and Distributed Data Processing and Analysis. The thesis covers Scientific Workflows and middlewares for High-Performance Computing and High-Throughput Computing.

This PhD is an example of the collaboration between the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science and Jülich Supercomputing Centre (JSC).

Members of the PhD commitee were Morris Riedel, Helmut Neukirchen, and Matthias Book, opponents were Ramin Yahyapour and Robert Lovas. The head of faculty, Rúnar Unnþórsson, was steering the defense. While we have some on cultural diversity involved, we need to improve on gender diversity! More photos can be found on flickr.

The DEEP-EST research project is at Vísindavaka, part of the European Researcher's Night, in Reykjavik, 28. September 2019.

Use the camera of your smartphone to detect objects in real-time. While neural networks are still best trained on a supercomputer, such as DEEP-EST with its Data Analysis Module, the trained neural network even runs in the browser of a smartphone. Bring your smartphone and objects such as apples, bananas or teddy bears to let your smartphone detect these objects.

Just open the following web page and allow your browser to use the camera: https://nvndr.csb.app/.

(Allow a few seconds for loading the trained model and initialisation.)

The used approach is Single Shot Detector (SSD) (the percentage shows how sure the neural network is about the classification) using the Mobilenet neural network architecture. The dataset used for training is COCO (Common Objects in Context), i.e. only objects of the labeled object classes contained in COCO will get detected. The Javascript code that is running in your browser uses Tensorflow Lite and its Object Detection API and model zoo.

If you want learn more about DEEP-EST, have a look at the poster below (click for PDF version):

University of Iceland was successful in a consortium applying for funding from the European Horizon 2020 research programme with the European Open Science Cloud (EOSC)-centric proposal EOSC-Nordic.

EOSC-Nordic aims to foster and advance the take-up of the European Open Science Cloud (EOSC) at the Nordic level by coordinating the EOSC-relevant initiatives taking place in Finland, Sweden, Norway, Denmark, Iceland, Estonia, Latvia, Lithuania, Netherlands and Germany. EOSC-Nordic aims to facilitate the coordination of EOSC relevant initiatives within the Nordic and Baltic countries and exploit synergies to achieve greater harmonisation at policy and service provisioning across these countries, in compliance with EOSC agreed standards and practices. By doing so, the project will seek to establish the Nordic and Baltic countries as frontrunners in the take-up of the EOSC concept, principles and approach. EOSC-Nordic brings together a strong consortium including e-Infrastructure providers, research performing organisations and expert networks, with national mandates with regards to the provision of research services and open science policy, and wide experience of engaging with the research community and mobilising national governments, funding agencies, international bodies and global initiatives and high-level experts on EOSC strategic matters.

A successful EOSC-Nordic will reinforce Nordic research area capability and competitiveness, create a profile of a leading knowledge based region, increase the ability of the region to attract talent and investments, enhance its appeal as a partner in cooperation, and strengthen the Nordic region and its efforts in the overall EOSC, through the creation of a cross-border cooperation model for Europe.

The project is coordinated by the Nordic e-Infrastructure Collaboration (NeIC) and the University of Iceland is one of the project participants. The University of Iceland's diverse team is lead by Ebba Þóra Hvannberg. Helmut Neukirchen and Morris Riedel contribute their knowledge with respect to e-Science, such as scalable, parallel machine learning, scientific workflows, and data federation. In addition to these researchers from the University's Computer Science department, experts from other departments of the University of Iceland contribute to EOSC-Nordic.

Project duration is 1st of September 2019 to 31st of August 2022. More information can be found on the EOSC-Nordic web page and also on my local page covering this research project.