Abstract

We would use the HPI Future SOC Lab resources to investigate the experimental evaluation of our activities on "DevOps-oriented Load and Resilience Testing for Microservices" and "Automated Cross-Component Issue Classification for Microservices". Novel aspect in Spring 2022: Use of GPUs for performance monitoring of AI-based systems.

Researchers

Principle Investigator: Dr.-Ing. Andre van Hoorn || Contact Author: Dr.-Ing. Andre van Hoorn || University of Hamburg, Germany

Abstract

We are planning to extend our work on PINPOINT (published at the Runtime and Operating Systems for Supercomputers workshop at Super Computing 2020) for energy-efficient and economic processing on heterogeneous compute infrastructures in the HPI Future SOC Lab in cooperation with the OSM Group (HPI).

Researchers

Principle Investigator: Prof. Dr.-Ing. habil Wolfgang Schröder-Preikschat || Contact Author: Prof. Dr.-Ing. Timo Hönig || Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Abstract

We design and develop EdgeIO, a hybrid resource orchestration and application deployment platform that seamlessly combines cloud and edge environments. The primary objective of EdgeIO is to impart flexibility -  in design, operation, moderation, and modification, all the while fully exploiting the capabilities of edge & cloud computing.

Researchers

Principle Investigator: Prof. Dr.-Ing. Jörg Ott || Contact Author: Dr. Nitinder Mohan || Technical University Munich, Germany

Abstract

Partial database replication is a query-driven approach to minimize the overall memory consumption of a replication cluster while still enabling a balanced load distribution among nodes. In this Future SOC Lab project, we want to deploy partial data allocations for large database clusters and evaluate their end-to-end performance.

Researchers

Principle Investigator: Prof. Dr. h.c. Hasso Plattner || Contact Author: Stefan Halfpap || Hasso Plattner Institute, Germany

Abstract

Our project contributes to the field of behaviour-based authentication. In the last years we collected a great amount of walking sequences of several people. As this dataset is too large to be processed on a normal machine, we hope to evaluate and improve our authentication model by the support of additional resources.

Researchers

Principle Investigator: Prof. Dr. Christoph Meinel || Contact Author: Vera Weidmann || neXenio GmbH, Germany

Abstract

In this project, we study mechanisms for integrating hardware accelerators in virtual machines and cloud infrastructures. Exemplary workloads include In-Memory Databases, scientific computation and multimedia applications. This project is a continuation of preceding projects conducted in the Spring and Fall Periods of 2018.

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Max Plauth || Hasso Plattner Institute, Germany

Abstract

Treating and preventing high blood pressure requires continuous monitoring. Leveraging photoplethysmography (PPG) sensors contained on wearables, machine learning algorithms can be used to estimate systolic as well as diastolic blood pressure values continuously and unobtrusively.

Researchers

Principle Investigator: Prof. Dr. med. Erwin Böttinger || Contact Author: Florian Hermes || Hasso Plattner Institute, Germany

Abstract

The project objectives are focused on research and development of methods, technologies, tools and software systems for efficient storage, processing, analysis, mining and visualization of Big mobility, transport and health-related data collected using mobile crowd sensing and Internet of Things paradigms in Smart Cities.

Researchers

Principle Investigator: Prof. Dragan Stojanovic || Contact Author: Prof. Dragan Stojanovic || University of Nis, Serbia

Abstract

This project is based on the consideration that novel research contributions like computing and network virtualisation, cloud computing, optimisation algorithms and AI-based policies can enable the development of self-adaptive edge computing services that flexibly self organise the edge layer.

Researchers

Principle Investigator: Dr Katja Gilly || Contact Author: Dr Katja Gilly || Miguel Hernández University, Spain

Abstract

The Deep-learning based Full Event Interpretation (DFEI) project is an ongoing initiative to increase the capabilities of the software trigger of the LHCb experiment at the LHC, by developing new deep-learning based algorithms. The huge complexity of the collision events requires the usage of HPC to allow the training of the algorithms.

Researchers

Principle Investigator: Prof. Marta Calvi || Contact Author: Ph.D. Julián García Pardiñas || University of Milan-Bicocca, Italy

Abstract

The WEEVIL9 project is designed as the extension of the previous one (WEEVIL8), which was developed using the RX600S5-1 server from the HPI Future SOC Lab.  This project is a continuation of previous one, that is, this submission is a renewal for accessing HPI Future SOC Lab infrastructure.

Researchers

Principle Investigator: Prof. Dr. Carlos Juiz || Contact Author: Belen Bermejo || Universitat de les Illes Balears, Spain

Abstract

We have built and ran all the initial building blocks for the large LSM model. We tested sixteen small RetNet(5x8,1) blocks consisting of 16 LSM columns built of 16640 biologically realistic, spiking HH neurons with 15 different input patterns. We would like to run larger models by incorporating Slurm and/or Docker into the pipeline.

Researchers

Principle Investigator: Dr. habil. (prof. PJAIT/UMCS) Grzegorz Marcin Wójcik || Contact Author: Karol Chlasta || Polish-Japanese Institute of Information Technology in Warsaw, Poland

Abstract

Our previous work applies Raman spectroscopy and DL for a fast diagnosis of diseases from salivary samples. We are now collecting and analizing data from PORTABLE spectrometers and apply AI interpretability techniques to gain further insights in the model's predictions. In addition, we will implement a software pipeline for the process automation.

Researchers

Principle Investigator: Full Professor Vincenzina Messina || Contact Author: Dario Bertazioli || Università degli Studi di Milano-Bicocca, Italy

Abstract

The aim of this project is to check the scalability of parallel, network intensive microbenchmarks and application written in Java, using the APGAS library and the PCJ library, HPC Challenge 2014 award-winning Java library for high-performance parallel computing, on the 1000 Core Cluster - with high performance Ethernet interfaces.

Researchers

Principle Investigator: Dr. Marek Nowicki || Contact Author: Dr. Marek Nowicki || Nicolaus Copernicus University in Toruń, Poland

Abstract

This project aims to formulate the Quantitative structure-property/activity relationship (QSPR/QSAR) model in terms of physicochemical properties, calculated by means of quantum mechanical methods, of C60 fullerene derivatives and doxorubicin. This drug has been widely used to treat breast cancer, isolated as well as carried with C60 fullerene.

Researchers

Principle Investigator: Research Professor (Dr.) Alan Joel Miralrio Pineda || Contact Author: Research Professor (Dr.) Alan Joel Miralrio Pineda || Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM), Mexico