2021


RAISE CoE Seminar: Accelerating ML with GraphCore
Morris Riedel, Pawel Gepner, Alexander Titterton
2021-11-23, Public YouTube Online Training and Teaching Seminar

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~3.73 MB (pdf) ]

[ CoE RAISE Relevance of GraphCore IPUs in CoE RAISE ~7.68 MB (pdf) ]

[ Thanks Slides ~1.88 MB (pdf) ]


Video in post production, to appear


RAISE CoE Seminar: Hyperparameter Tuning with Ray Tune
Morris Riedel, Marcel Aach
2021-10-29, Public YouTube Online Training and Teaching Seminar

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~3.71 MB (pdf) ]

[ CoE RAISE Relevance of Hyperparameter Tuning in CoE RAISE ~8.40 MB (pdf) ]

[ Introduction to Hyperparameter Tuning and Neural Architecture Search ~12.4 MB (pdf) ]

[ Thanks Slides ~2.02 MB (pdf) ]


Video in post production, to appear


RAISE CoE Seminar: MLOps with ClearML
Morris Riedel, Kurt de Grave
2021-09-30, Public YouTube Online Training and Teaching Seminar

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~3.66 MB (pdf) ]

[ CoE RAISE Role of MLOps ~8.56 MB (pdf) ]

[ Introduction to MLOps with ClearML ~6.52 MB (pdf) ]

[ Thanks Slides ~1.99 MB (pdf) ]


Video in post production, to appear


RAISE CoE Seminar: Brief Introduction to Autoencoders
Morris Riedel, Rakesh Sarma
2021-08-31, Public YouTube Online Training and Teaching Seminar

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~3.69 MB (pdf) ]

[ CoE RAISE Role of Autoencoders ~6.34 MB (pdf) ]

[ Autoencoders – A Brief Introduction and Overview ~8.29 MB (pdf) ]

[ Thanks Slides ~1.99 MB (pdf) ]


Video in post production, to appear


RAISE CoE Seminar: Distributed Deep Learning
Morris Riedel, Rocco Sedona, Marc Sergent, Marcel Aach
2021-07-29, Public YouTube Online Training and Teaching Seminar

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~3.71 MB (pdf) ]

[ CoE RAISE Need for Distributed Deep Learning Slides ~9.78 MB (pdf) ]

[ Thanks Slides ~2.13 MB (pdf) ]


Video in post production, to appear


RAISE CoE Seminar: High Performance Data Analytics with the Helmholtz Analytics Toolkit (HeAT)
Morris Riedel, Claudia Comito, Charlotte Debus
2021-06-28, Public YouTube Online Training and Teaching Seminar
Abstract
The CoE RAISE project develops many AI methods in nine compute-intensive and data-intensive use cases. The use case researchers leverage various AI tools on heterogeneous high-performance computing (HPC) systems and co-design the RAISE unique AI framework towards Exascale. The seminar demonstrates how CoE RAISE and other computational-intensive and data-intensive communities can benefit from the free Helmholtz Analytics Toolkit (HeAT). The goal of HeAT is to fill the gap between data analytics and machine learning libraries with a strong focus on single-node performance on the one hand and traditional HPC on the other. HeAT’s generic Python-first programming interface integrates seamlessly with the existing data science ecosystem in CoE RAISE. It makes it as effortless as using NumPy to write scalable scientific and data science applications. The seminar provides a sophisticated introduction to Heat and its use cases and discusses a possible adoption of HeAT in the RAISE unique AI framework design.

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~3.74 MB (pdf) ]

[ CoE RAISE Towards Unique AI Framework Methodologies Slides ~7.94 MB (pdf) ]

[ Thanks Slides ~2.57 MB (pdf) ]


Video in post production, to appear


RAISE CoE Seminar: Git-based Data Management with the Open-Source DataLad Tool
Morris Riedel, Michael Hanke, Kaustubh Patil
2021-05-28, Public YouTube Online Training and Teaching Seminar
Abstract
The seminar demonstrates how CoE RAISE and other computational-intensive and data-intensive communities can benefit from the free DataLad tool. It enables researchers to discover data since it has built-in support for metadata extraction and search. HPC & AI researchers often consume data in different ways requiring direct access to individual files, especially when using a few files from some large datasets for analysis. DataLad enables that and supports also sharing datasets with the public or just some colleagues on platforms without the need for a central service for publishing datasets. Version control systems such as GIT are a de-facto standard for open-source software development. A similar level of tooling enables the DataLad tool for data management and analysis. HPC & AI researchers benefit from comprehensively track the exact state of any analysis inputs that produced results across the entire lifetime of a project and multiple datasets, enabling reproducibility.

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~4.41 MB (pdf) ]

[ CoE RAISE Use Case Foundations and Requirements for Data Management Slides ~7.84 MB (pdf) ]

[ Thanks Slides ~2.54 MB (pdf) ]



RAISE CoE Online Seminar: HPC Systems Engineering in the Interaction Room
Morris Riedel, Matthias Book, Helmut Neukirchen
2021-04-08, Public YouTube Online Training and Teaching Seminar
Abstract
The recently started RAISE CoE EU project works on nine different engineering use cases (see: https://www.coe-raise.eu/use-cases) that bring researchers and experts from the industry together to co-design intertwined AI and HPC methodologies for the Exascale era. These methodologies’ goal is to be usable by a wide variety of scientific and engineering applications to enable AI at Exascale (see: https://www.coe-raise.eu/ai-exascale).

One key element of the above CoE project co-design process is to perform HPC systems engineering in the Interaction Room because the design, development, and deployment of scientific computing applications are very complex holistically representing a complicated system. It requires scientific, industry, HPC, AI, and excellent software engineering expertise to enable scalability for Exascale. The cooperation and communication between experts from these quite different disciplines can be difficult.

This seminar informs about the general Interaction Room technique that facilitates interdisciplinary collaboration in complex software projects, emphasizing intertwined AI and HPC. An Interaction Room is a (physical or virtual) room that is outfitted with several large analogue or digital whiteboards known as canvases. They are used to visualize and facilitate discussion of critical aspects of a complex software system. Each canvas is dedicated to modeling a particular perspective on the system. The key difference to other modelling techniques is that models in the Interaction Room are kept deliberately informal. Hence, the goal is not to create a perfect specification but to encourage stakeholders from diverse backgrounds to discuss those aspects that are essential to the software project’s success.

The seminar demonstrates how the CoE RAISE aims to perform co-design with this Interaction Room technique to understand the domain requirements, understand technical restrictions, identify aspects of particular scientific value, and identify the most critical risks of those projects. The seminar further outlines initial lessons learned in intertwined HPC and AI applications. Using the Interaction Room at an early project stage helps prevent costly misunderstandings and oversights later on and has already proven helpful in numerous complex information systems projects.

[ Video in RAISE Public YouTube Channel ]

[ Welcome Slides ~5.14 MB (pdf) ]

[ CoE RAISE Use Case Foundations and Lessons Learned from Fact Sheets Slides ~8.28 MB (pdf) ]

[ Thanks Slides ~3.37 MB (pdf) ]

RAISE CoE Online Seminar: HPC Systems Engineering in the Interaction Room


Talk at IPDPS HCW 2021: Practice and Experience in Using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures
Morris Riedel
Heterogenity in Computing Workshop (HCW) held in conjunction of the
35th IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Portland, Virtual Conference,
2021-05-17 – 2020-05-21
[ EVENT ] [ Slides ~21.3 MB (pdf) ]

Practice and Experience in Using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures


Short Introduction to DataLad and Juelich Activities
Morris Riedel
2021-02-03, Public YouTube Online Training and Teaching Seminar, Public Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL 2021) Winterschool for Online Learning, BigBrain Data and Tools February 3 – 4, 2021, Virtual with YouTube Lectures

[ Event ] [ Slides ~4.81 MB (pdf) ]

Short Introduction to DataLad and Juelich Activities

2020

Chairing Euro-Par 2020 Best Paper Session
Morris Riedel
Topic Chair Data Management, Analytics and Deep Learning
26th Euro-Par Conference, August 24-28, 2020, Warsaw, Poland
[ Event ]

Chairing Euro-Par 2020 Best Paper Session

Chairing Euro-Par 2020 Topic 05 Data Management – Euro-Par 2020 Session
Morris Riedel
Topic Chair Data Management, Analytics and Deep Learning
26th Euro-Par Conference, August 24-28, 2020, Warsaw, Poland
[ Event ]

Chairing Euro-Par 2020 Topic 05 Data Management – Euro-Par 2020 Session

UTmessan 2020 – Demystifying Quantum Computing
Morris Riedel
UTmessan 2020, February 7, 2020, Harpa, Reykjavik, Iceland
[ Event ]

UTmessan 2020 – Demystifying Quantum Computing

2017

Invited Tutorial – Deep Learning using a Convolutional Neural Network
Morris Riedel
Ghent University, six lectures including exercises
2017-11-30 – 2017-12-01
[ Event ]

Lecture 1 – Deep Learning Fundamentals and GPGPUs
Lecture 2 – Convolutional Neural Networks and Tools
Lecture 3 – Convolutional Neural Network Applications
Lecture 4 – Convolutional Neural Network Challenges
Lecture 5 – Transfer Learning Technique
Lecture 6 – Other Learning Models & Summary

Invited Tutorial – Introduction to Machine Learning Algorithms
Morris Riedel
Ghent University, six lectures including exercises
2017-11-23 – 2017-11-24
[ Event ]

Lecture 1 – Machine Learning Fundamentals
Lecture 2 – Unsupervised Clustering and Applications
Lecture 3 – Supervised Classification and Applications
Lecture 4 – Classification Challenges and Solutions
Lecture 5 – Regularization and Support Vector Machines
Lecture 6 – Validation and Parallelization Benefits

PRACE 2017 Spring School – Introduction to Parallel and Scalable Machine Learning – Basics
Morris Riedel
The Cyprus Institute, April, 25 – 27, 2017, Nicosia, Cyprus
[ Event ]

PRACE 2017 Spring School – Introduction to Parallel and Scalable Machine Learning – Basics

PRACE 2017 Spring School – Introduction to Parallel and Scalable Machine Learning – Parallelization Benefits
Morris Riedel
The Cyprus Institute, April, 25 – 27, 2017, Nicosia, Cyprus
[ Event ]

PRACE 2017 Spring School – Introduction to Parallel and Scalable Machine Learning – Parallelization Benefits

2016

UTmessan 2016 – Societal Impact of High Performance Computing in Science and Engineering
Morris Riedel
UTmessan 2016, February 5 – 6, 2016, Harpa, Reykjavik, Iceland
[ Event ]

UTmessan 2016 – Societal Impact of High Performance Computing in Science and Engineering

2015

PRACE XSEDE Interoperability Projects – Smart Data Analytics for Earth Sciences across XSEDE and PRACE
Morris Riedel
ECSS Online Webinar, March 6, 2015
(Talk starts at 32 minutes 11 seconds)
[ Event ]

PRACE XSEDE Interoperability Projects – Smart Data Analytics for Earth Sciences across XSEDE and PRACE

2011

Interview: Grid Interoperation Now GIN OGF Working Group at OGF31 as GIN CG Co Chair
Morris Riedel
Open Grid Forum (OGF) 31 Conference, March 21, 2011, Taipei, Taiwan
[ Event ]

Interview: Grid Interoperation Now GIN OGF Working Group at OGF31 Morris Riedel Forschungszentrum Jülich GIN CG Co Chair

2009

Interview: Production Grid Infrastructure PGI OGF Working Group at OGF25 as PGI WG Co Chair
Morris Riedel
Open Grid Forum (OGF) 25 Conference, March 2 – 3, 2009, Catania, Italy
[ Event ]

Interview: Production Grid Infrastructure PGI OGF Working Group at OGF25 Morris Riedel Forschungszentrum Jülich PGI WG Co Chair