Horizon 2020 Future and Emerging Technologies programme: Dynamical Exascale Entry Platform - Extreme Scale Technologies (DEEP-EST) (2017-2021)

The DEEP-EST ("Dynamical Exascale Entry Platform - Extreme Scale Technologies") project is funded as part of the European Comission's Horizon 2020 ambitious Future and Emerging Technologies (FET) programme in order to create the blueprints of the next generation ("pre-exascale") supercomputer hardware and software ("co-design").

DEEP-EST will create a first incarnation of the Modular Supercomputer Architecture (MSA) and demonstrate its benefits. In the spirit of the preceding DEEP and DEEP-ER projects, the MSA integrates compute modules with different performance characteristics into a single heterogeneous system. Each module is a parallel, clustered system of potentially large size. A federated network connects the module-specific interconnects. MSA brings substantial benefits for heterogeneous applications/workflows: each part can be run on an exactly matching system, improving time to solution and energy use. This is ideal for supercomputer centres running heterogeneous application mixes (higher throughput and energy efficiency). It also offers valuable flexibility to the compute providers, allowing the set of modules and their respective size to be tailored to actual usage.

The DEEP-EST prototype will include three modules: general purpose Cluster Module and Extreme Scale Booster supporting the full range of HPC applications, and Data Analytics Module specifically designed for high-performance data analytics (HPDA) workloads. Proven programming models and APIs from HPC (combining MPI and OmpSs, an OpenMP extension) and HPDA will be extended and combined with a significantly enhanced resource management and scheduling system to enable straightforward use of the new architecture and achieve highest system utilisation and performance. Scalability projections will be given up to the Exascale performance class. The DEEP-EST prototype will be defined in close co-design between applications, system software and system component architects. Its implementation will employ European integration, network and software technologies. Six ambitious and highly relevant European applications from HPC and HPDA domains will drive the co-design, serving to evaluate the DEEP EST prototype and demonstrate the benefits of its innovative Modular Supercomputer Architecture.

The University of Iceland’s team is lead by Helmut Neukirchen and Morris Riedel. Gabriele Cavallaro support as PostDoc our work and Ernir Erlingsson is contributing a PhD student. Our team will perform ambitious research by providing as a case study scientific parallel software applications from the field of machine learning with a focus on applications in the Earth Science's domains. Project duration is 1st of July 2017 to 31st of March 2021.

Project Web page


Ernir Erlingsson, Gabriele Cavallaro, Morris Riedel, Helmut Neukirchen.
Scaling Support Vector Machines Towards Exascale Computing for Classification of Large-Scale High-Resolution Remote Sensing Images.
Short paper at 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 23-27 2018, Valencia, Spain, 2018.

Ernir Erlingsson, Gabriele Cavallaro, Andreas Galonska, Morris Riedel, Helmut Neukirchen.
Modular Supercomputing Design supporting Machine Learning Applications.
The 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2018), Conference DS-DC -- Data and Life Sciences supported by Distributed Computing, 21-25 May 2018, Opatija/Abbazia, Croatia, 2018.

Ernir Erlingsson, Gabriele Cavallaro, Morris Riedel, Helmut Neukirchen.
Scaling DBSCAN towards exascale computing for clustering of big data sets.
Abstract at European Geosciences Union (EGU) General Assembly 2018 (session IE4.1/NP4.3/AS5.13/CL5.18/ESSI2.3/GD10.6/HS3.7/NH11.14/SM7.03 – Big data and machine learning in geosciences, Vienna, Austria, 8–13 April 2018. Geophysical Research Abstracts, Copernicus Publications, 2018.

Ernir Erlingsson, Gabriele Cavallaro, Morris Riedel, Helmut Neukirchen.
Enhancing Deep Learning towards Exascale with the DEEP-EST Modular Supercomputer Architecture.
Abstract at 5th Exascale Applications and Software Conference (EASC 2018), Edinburgh, Scotland, 17-19 April 2018. 2018 .

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.

Helmut Neukirchen.
Software Engineering Challenges of the H2020 DEEP-EST Modular Supercomputing Architecture.
Presentation at Computing Science Colloquium at University of Göttingen, November 7 2017, Göttingen, Germany