Funktionen

Im Folgenden finden Sie eine Aufstellung der zur Verfügung stehenden Themen. Die angegebene Literatur versteht sich als Startlektüre und weitere Literatur sollte selbstständig recherchiert wertden.

The suggested topics are listed below. The literature indicated here is intended as a starting point only - further literature should be researched independently.

1) Intel's Global Extensible Open Power Manager (GEOPM)

A framework for power/energy optimizations targeting High Performance Computing (HPC) by dynamic coordination of hardware settings across system compute nodes utilized by a given parallel application in response to the application's behavior and requests from the resource management and scheduling system.

• GEOPM - Global Extensible Open Power Manager
• Eastep, Jonathan, Steve Sylvester, Christopher Cantalupo, Federico Ardanaz, Brad Geltz, Asma Al-Rawi, Fuat Keceli, and Kelly Livingston. "Global extensible open power manager: a vehicle for HPC community collaboration toward co-designed energy management solutions." Supercomputing PMBS (2016).

2) Parallel and Distributed Deep Learning

Deep Neural Networks (DNNs) are becoming an important vehicle for modern large-scale computing applications, while their training in most cases still requires a significant amount of time. The topic discusses the training problem and describes possible approaches for its parallelization.

• Ben-Nun, Tal, and Torsten Hoefler. "Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis." arXiv preprint arXiv:1802.09941 (2018).

3) Tensor Processing Units (TPUs) and their In-Datacenter Deployment Analysis

A Tensor Processing Unit (TPU) is application-specific integrated circuit (ASIC) developed by Google for accelerating the inference phase of artificial neural networks. The topic describes these AI accelerators and discusses their indatacenter performance.

• Jouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates et al. "In-datacenter performance analysis of a tensor processing unit." In Computer Architecture (ISCA), 2017 ACM/IEEE 44th Annual International Symposium on, pp. 1-12. IEEE, 2017.

4) Container technologies (e.g. Singularity, Docker, Charliecloud, Shifter)

Containers, being instances of an Operating System (OS) level virtualization, are getting more appealing due to their higher efficiency as compared to the full, hardware-level, virtualization. The topic describes the concept of the containers, and discusses the differences among main software containers like Singularity, Docker, Kubernetes, Charliecloud, and Shifter that are currently used in HPC.

• Abdelbaky, Moustafa, Javier Diaz-Montes, Manish Parashar, Merve Unuvar, and Malgorzata Steinder. "Docker containers across multiple clouds and data centers." In Utility and Cloud Computing (UCC), 2015 IEEE/ACM 8th International Conference on, pp. 368-371. IEEE, 2015.
• Kurtzer, Gregory M., Vanessa Sochat, and Michael W. Bauer. "Singularity: Scientific containers for mobility of compute." PloS one 12, no. 5 (2017): e0177459.
• Charliecloud

5) Deep Neural Networks for Malware Detection in Executables

As the use of computing devices increases in every day life, malware detection continues to remain a serious challenge for corporations, governmental agencies, and individuals. Today malware detection systems still heavily rely on heuristic and signature-based methods, where signature represents set of rules that are generally specific and thus usually fail to capture a new malware. This topic discusses an alternative approach, that uses neural networks and the raw bytes of the binary program itself to determine maliciousness without executing the target application.

• David, Omid E., and Nathan S. Netanyahu. "Deepsign: Deep learning for automatic malware signature generation and classification." In Neural Networks (IJCNN), 2015 International Joint Conference on, pp. 1-8. IEEE, 2015.
• Raff, Edward, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, and Charles K. Nicholas. "Malware detection by eating a whole exe."

6) Explainable System Predictive Maintenance

This topic discusses various Machine Learning based approaches aimed at detection of anomalies within HPC system and surrounding ecosystem. In doing so, a special focus will be attributed to the explainability of the discussed predictive modeling solutions.

• Alvaro Frank, Dai Yang, Tim Suss, Martin Schulz, Andre Brinkmann. "Reducing False Node Failure Predictions in HPC"

7) Intel oneAPI

Current HPC workloads are becomming more data-driven. This pushes further the need for diverse computer architectures (e.g. CPU, GPU, FPGA, etc.) in order to deliver the required compute performance for the various specialized data-centric workloads. However, taking advantage of multiple HW architectures is a challenge for developers, as it is complex and time-intensive due to the lack of code-reuse possibilities; appropriate programming models that do not degrade the application performance; etc. Intel's oneAPI is a single unified programming model that aims to ease the development accross multiple architectures. This topic introduces the basics oneAPI programming model and discusses its applicability across various HW architectures.

• Intel oneAPI Toolkit

8) Contemporary HPC and the World's Fastest Supercomputer Summit

The topic discusses the supercomputing today by analyzing current TOP500 list and identifying the major trends. Additionally, it describes Summit system, deployed at Oak Ridge National Laboratory and recognized as the world's fastest computer back in June' 18 during International Supercomputing Conference by delivering 122.3 PFLOPS performance during the LINPACK run. Since then the system has been upgraded to deliver 143.5 PFLOPS LINPACK performance making it still the fastest supercomputer in the world (TOP500 November'19 rankings). The topic discusses the system design aspects and describes its compute and cooling infrastructure.

• TOP500 List of Supercomputers
• Summit at Oak Ridge National Laboratory

9) RISC-V

RISC-V (pronounced as Risk-Five) is a free and open reduced instruction set computer (RISC) Instruction Set Architecture (ISA). Originated in UC Berkeley back in 2010, RISC-V is continuously gaining its popularity due to its open and modular design. This topic provides introduces and provides a high-level overview of the RISC-V standard.

• RISC-V: The Free and Open RISC Instruction Set Architecture

10) Kubernetes

The topic discusses the open-source container orchestration system, Kubernetes, that assists developers in the deployement, scaling, and management of their large-scale applications.

• Kubernetes: Production-Grade Container Orchestration
• Marko Luksa. Kubernetes in Action

11) Introduction to Quantum Computing

The topic introduces the basics of qunatum computing and discusses its current state, the applicability range, challenges, and perspectives.

• Phillip Kaye, Raymond Laflamme, Michele Mosca. "An Introduction to Quantum Computing"

12) Machine Learning Based Classification Over Encrypted Data

The HPC systems today enable the application of Machine Learning (ML) based methods in various domains, ranging from face recognition to medical or genomics predictions, as they provide the required data storage and computational power. However, many of these ML based applications rely on sensitive data usage making it important to control the privacy of the considered data and the underlying classifier. The topic discusses ML based classification techniques over encrypted data. 

• Bost, Raphael, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser. "Machine learning classification over encrypted data." In NDSS. 2015.
• Intel's HE-Transformer for nGraph

13) Cross-Architectural Modelling of Power Consumption Using Neural Networks

Energy consumption is becoming a dominating factor for the Total Cost of Ownership of many supercomputers, making it important to keep energy costs in budget and to operate within available capacities of power distribution and cooling systems. The topic considersprediction of power consumption of HPC systems utilizing artificial neural networks that use data obtained from hardware performance counters. The topic discusses the accuracy of the proposed, portable across different micro-architectureimplementations, methodology and outlines the advantages against its simpler, linear-regression based, counterparts.

• Elisseev, Vadim V., Milos Puzovic, and Eun Kyung Lee. "A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks." Supercomputing Frontiers and Innovations 5, no. 4 (2018): 24-41.

14) Next generation arithmetic for HPC and AI

Posit arithmetic, a form of universal number (unum) computer arithmetic, is designed as a direct drop-in replacement for IEEE Standard 754 floating-point numbers (floats), provide compelling advantages over floats, including larger dynamic range, higher accuracy, better closure, bitwise identical results across systems, simpler hardware, and simpler exception handling.Posits never overflow to infinity or underflow to zero, and 'Nota-Number' (NaN) indicates an action instead of a bit pattern. The topic discusses the posit arithmetic and outlines its advantages against fixed-pointarithmetic approaches currently used for AI and signal processing.

• Gustafson, John L., and Isaac T. Yonemoto. "Beating floating point at its own game: Posit arithmetic." Supercomputing Frontiers and Innovations 4, no. 2 (2017): 71-86.

15) Explainable Aritficial Intelligence (XAI): Opportunities and Challenges

Recent years have witnessed the extension of applicatibility range of artificial intelligence (AI) to different domains. In fact, AI is already assisting humanity in building better security systems, efficient transportation and web searches, personalized advertising, etc. Sometimes, it even decides whether a given person should be approved for a bank loan. However, the lack of complete explainability of various approaches serves as an obstacle for the further deployement of the proposed AI-based solutions. This topic will discuss the concepts of explainable AI (XAI), and outline the existing opportunities and challenges.

• Arrieta, Alejandro Barredo, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Gaia et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI."

16) AI for Detecting Breast Cancer: explainability and accuracy

The topic reviews the state-of-the-art Artificial Intelligence (AI) based solutions for breast cancer detection, dicusses their explainability and outlines the currently existing limitations.

• Lamy, Jean-Baptiste, Boomadevi Sekar, Gilles Guezennec, Jacques Bouaud, and Brigitte Seroussi. "Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach."
• Le, E. P. V., Y. Wang, Yuan Huang, S. Hickman, and F. J. Gilbert. "Artificial intelligence in breast imaging."

17) Elasticsearch

Nowdays, when checking emails, when purchasing from an online store, when reading a document, we always expect to have a time-efficient search engine working for us flawlessly in the background. Moreover, we typically expect these engines to be intelligent enough to support us with various manipulations with our (pending) search results (e.g. sorting, suggesting, etc.). Elasticsearch is an open-source search engine library developed in Java. This topic introduces the basic concepts and principles of Elasticsearch, and outlines how typical search problems can be tackled with the help of Elasticsearch.

• Get Started with Elasticsearch
• Gheorghe, Radu, Matthew Lee Hinman, and Roy Russo. Elasticsearch in action.

18) Hadoop

In the current era of big data, where understanding of various complex systems continuously pushes the need for the generation of even larger data volumes, makes the efficient data processing and storage an imperative and natural requirement. This topic discusses the Hadoop platform that allows for distributed storage and data processing. The topic introduces Hadoop architecture, provides a high-level overview of its functionalities, and outlines its limitations.

• Holmes, Alex. Hadoop in practice (second edition).