A5102830101- Advanced Data Storage Technologies
- Parallel Computing and Optimization Techniques
- Scientific Computing and Data Management
- Distributed and Parallel Computing Systems
- Caching and Content Delivery
- Software Engineering Techniques and Practices
- Machine Learning in Materials Science
- Cloud Computing and Resource Management
- Technology Adoption and User Behaviour
- Neural Networks and Reservoir Computing
- Computational Physics and Python Applications
- Construction Project Management and Performance
- Anomaly Detection Techniques and Applications
- Software System Performance and Reliability
- Customer Service Quality and Loyalty
- BIM and Construction Integration
Technical University of Darmstadt
2022-2025
Shingled magnetic recording (SMR) is a data storage technology used in modern hard disk drives (HDDs) to increase the areal density capacity (ADC) of underlying media. The research on SMR began around 2008, with first entering market 2013. We have performed an extensive survey research, encompassing over 100 scientific papers spanning nearly 17 years. Our offers in-depth analysis evolution disks, examining different types architectures and inherent performance challenges existing disks. also...
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into real-time experimental data processing loop to accelerate scientific discovery. The material report builds on two workshops held by Fast Science covers three main areas: across a number domains; training implementing performant resource-efficient algorithms; computing architectures, platforms, technologies deploying these...
With the trend towards ever larger “big data” applications, many of gains achievable by using specialized compute accelerators become diminished due to growing I/O overheads. While there have been several research efforts into computational storage and FPGA implementations NVMe interface, our knowledge, only very limited move parts Linux block stack FPGA-based hardware accelerators. Our hardware/software framework DeLiBA initially addressed this deficiency allowing high-productivity...
With the trend towards ever larger "big data" applications, many of gains achievable by using specialized compute accelerators become diminished due to growing I/O overheads. While there have been a number research efforts into computational storage and FPGA implementations NVMe interface, our knowledge only very limited move parts Linux block stack FPGA-based hardware accelerators. Our hardware/software framework DeLiBA aims address this deficiency allowing high-productivity development...