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) Modern accelerator technologies for machine learning applications

Accelerator technologies, that work alongside CPUs of the compute servers for accelerating certain regions of an application requiring a large amount of numerical operations, are becoming more broadly used and adopted in HPC systems. This topic discusses different contemporary accelerator technologies (GPUs, TPUs, etc.) that are used for boosting the performance of various machine learning applications. In addition, the topic examines the energy-efficiency of the considered accelerator technologies, provides an analysis of state-of-the-art benchmarks used for their evaluation, and suggests a summary outlining the main differences of the considered accelerators.

• Reuther, Albert, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, and Jeremy Kepner. "Survey and benchmarking of machine learning accelerators." In 2019 IEEE high performance extreme computing conference (HPEC), pp. 1-9. IEEE, 2019.
• Wang, Yuxin, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, and Xiaowen Chu. "Benchmarking the performance and energy efficiency of ai accelerators for ai training." In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 744-751. IEEE, 2020.

2) Introduction to Field-Programmable Gate Arrays (FPGAs)

The topic provides an introduction to FPGAs, outlines their purpose and usability as well as discusses different design flows and concepts.

• Maxfield, Clive. The design warrior's guide to FPGAs: devices, tools and flows. Elsevier, 2004.
• Andina, Juan Jose Rodriguez, Eduardo De la Torre Arnanz, and Maria Dolores Valdes. FPGAs: fundamentals, advanced features, and applications in industrial electronics. CRC Press, 2017

3) Arm Scalable Vector Extension version 2 (SVE2)

The topic introduces Arm Scalable Vector Extension version 2 (SVE2) which builds on top of the SVE (v1) - technology utilized by the processors of today's fastest (as of November 2020 Top500 rankings) supercomputer: Fugaku. SVE2 is used by Arm's latest ARMv9-A architecture for further processing enhancement of ML workloads. The topic outlines the fundamentals of SVE2 architecture, describes the new features, as well as discusses the power-efficiency aspects.

• What is the Scalable Vector Extension?
• Introduction to SVE2

4) Contemporary HPC and the World's Fastest Supercomputer Fugaku

The topic discusses the supercomputing today by analyzing current TOP500 list and identifying the major trends. Additionally, it describes Fugaku system, deployed at RIKEN Center for Computational Science (Japan) and currently recognized as the world's fastest supercomputer (TOP500 November'20 rankings). The topic discusses the system design aspects, describes its compute and cooling infrastructures, and examines the energy-efficiency.

• TOP500 List of Supercomputers
• Sato, Mitsuhisa. "The Supercomputer Fugaku and Arm-SVE enabled A64FX processor for energy-efficiency and sustained application performance." In 2020 19th International Symposium on Parallel and Distributed Computing (ISPDC), pp. 1-5. IEEE, 2020.
• Kodama, Yuetsu, Tetsuya Odajima, Eishi Arima, and Mitsuhisa Sato. "Evaluation of Power Management Control on the Supercomputer Fugaku." In 2020 IEEE International Conference on Cluster Computing (CLUSTER), pp. 484-493. IEEE, 2020.

5) Emerging Non-Volatile Memory (NVM) Technologies

The topic discusses different contemporary NVM technologies, introduces their design, architecture, and outlines corresponding applications.

• Chen, An. "A review of emerging non-volatile memory (NVM) technologies and applications." Solid-State Electronics 125 (2016): 25-38.
• Hong, Seungbum, Orlando Auciello, and Dirk Wouters, eds. Emerging non-volatile memories. Springer, 2014.

6) Ensemble learning

This topic introduces the concept of ensemble learning - a technique for combining the results of several models trained on the same data set for the increase of the overall prediction accuracy.

• Witten, I. H., E. Frank, M. A. Hall, and C. J. Pal. "Chapter 12-Ensemble learning." Data Mining (Fourth Edition). Morgan Kaufmann (2017): 467-478.
• Zhang, Cha, and Yunqian Ma, eds. Ensemble machine learning: methods and applications. Springer Science & Business Media, 2012.

7) Transfer learning

This topic introduces the concept of transfer learning, discusses its theory and the statistical guarantees.

• Yang, Qiang, Yu Zhang, Wenyuan Dai, and Sinno Jialin Pan. Transfer learning. Cambridge University Press, 2020.
• Tripuraneni, Nilesh, Michael I. Jordan, and Chi Jin. "On the theory of transfer learning: The importance of task diversity." Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020).
• Neyshabur, Behnam, Hanie Sedghi, and Chiyuan Zhang. "What is being transferred in transfer learning?" Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

8) Beyond supervised and unsupervised learning

This topic discusses machine learning paradigms beyond classical supervised and unsupervised learning, and outlines best practices for algorithm design taking advantage of such learning attempts.

• Witten, I. H., E. Frank, M. A. Hall, and C. J. Pal. "Chapter 11-Beyond supervised and unsupervised learning." Data Mining (Fourth Edition). Morgan Kaufmann (2017): 467-478.
• Zhu, Xiaojin, and Andrew B. Goldberg. "Introduction to semi-supervised learning." Synthesis lectures on artificial intelligence and machine learning 3, no. 1 (2009): 1-130.

9) Quantum machine learning

Topic discusses the aspects of computational speed and data storage improvements with the help of quantum-enhanced machine learning. This topic also introduces the TensorFlow Quantum library.

• Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. "Quantum machine learning." Nature 549, no. 7671 (2017): 195-202.
• TensorFlow Quantum

10) Evaluating unsupervised learning methods for outlier detection

Data sanitization is an essential step when building effective machine learning based models. This topic discusses different unsupervised learning methods for outlier detection, analysis their differences as well as outlines their advantages and disadvantages.

• Campos, Guilherme O., Arthur Zimek, Jörg Sander, Ricardo JGB Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael E. Houu le. "On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study." Data mining and knowledge discovery 30, no. 4 (2016): 891-927.
• Domingues, Rémi, Maurizio Filippone, Pietro Michiardi, and Jihane Zouaoui. "A comparative evaluation of outlier detection algorithms:: Experiments and analyses." Pattern Recognition 74 (2018): 406-421.

11) Deep Semi-Supervised Anomaly Detection

This topic continues the discussion on anomaly detection. It investigates the cases where in addition to large set of unlabeled data, one has an access to a small labeled data. The topic discusses the possibilities of maximizing the advantage of the available labeled data using semi-supervised learning approach for the overall increase of the classification accuracy.

• Ruff, Lukas, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft. "Deep Semi-Supervised Anomaly Detection." In International Conference on Learning Representations (ICLR). 2019.

12) Unsupervised translation of programming languages

The topic introduces a machine learning based transcompiler and discusses its generalization.

• Roziere, Baptiste, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. "Unsupervised translation of programming languages." Advances in Neural Information Processing Systems 33 (2020).

13) Generating face images from sketches

This topic discusses recent machine learning based approaches that transform human drawn sketches to images.

• Chen, Shu-Yu, Wanchao Su, Lin Gao, Shihong Xia, and Hongbo Fu. "DeepFaceDrawing: Deep generation of face images from sketches." ACM Transactions on Graphics (TOG) 39, no. 4 (2020): 72-1.

14) Edge computing: is the era of traditional data centers coming to an end?

The topic introduces the edge computing paradigm, outlines current trends and challenges, and discusses the impact this distributed computing paradigm might have on future of the data centers.

• Shi, Weisong, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. "Edge computing: Vision and challenges." IEEE internet of things journal 3, no. 5 (2016): 637-646.
• Satyanarayanan, Mahadev. "The emergence of edge computing." Computer 50, no. 1 (2017): 30-39.

15) Building your own storage cloud with Raspberry Pi and external hard drives

This topic discusses ways for deploying a secure and robust storage cloud using open-source frameworks on Raspberry Pi. The report provides a detailed analysis of the required open-source frameworks as well as evaluates their ease of deployment, security, extendability as well as assesses the efforts required for operation and maintenance.

• ownCloud
• Nextcloud

16) Performance analysis of cloud applications

This topic discusses various strategies and techniques for analysing the performance of large-scale cloud applications.

• Ardelean, Dan, Amer Diwan, and Chandra Erdman. "Performance analysis of cloud applications." In 15th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 18), pp. 405-417. 2018.

17) Container orchestration tools: advantages, disadvantages, and challenges

This topic discusses contemporary container orchestration/management platforms and outlines their advantages and disadvantages. Furthermore, the topic touches upon the aspects of deployment and operation challenges, as well as discusses how those help with the development of AI microservices.

• Amazon Elastic Container Service (ECS)
• Kubernetes
• Google Kubernetes Engine
• SaltStack

18) Elasticsearch

Nowadays, 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.