| 169 | | == Optimizing imaging algorithms to the latest parallel CPU and GPU architectures == |
| 170 | | * Contact person: Suren A. Chilingaryan csa@dside.dyndns.org |
| 171 | | * [raw-attachment:1301-gpu-optimization-v2.pdf Detailed announcement] |
| 172 | | * Required Skills: Good knowledge of C programming language, knowledge of OpenCL or/and CUDA is a plus |
| 173 | | * Experience Gained: Parallel programming, GPU programming, Image processing |
| 174 | | Parallel computing has become increasingly important in the last several years. The standard servers include nowadays up to 64 computing cores. Modern GPUs are able to execute thousands of floating point operations in parallel and have become a valuable tool in multiple scientific field that require high computational throughput. It becomes more and more important to parallelize existing image processing algorithms and tune the implementations to the recent hardware architectures. It is crucial to take into the consideration the details of hardware architectures. The computational units may employ different types of cache hierarchies to accelerate memory access, the new processors often introduce new sets of instructions accelerating specific operations. |
| 175 | | The student will select an algorithm from one of the ongoing projects and perform optimization and tuning for the used hardware. Available options include differential phase contrast imaging done in cooperation with ANKA synchrotron, digital image correlation and tracking done in collaboration with University of Pennsylvania, X-Ray CT done in collaboration with Helmholtz Center in Dresden-Rosendorf. |
| | 169 | == A scalable storage platform for large archives of multi-dimensional meteorological data == |
| | 170 | * Contact person: Nicholas Tan Jerome <nicholas.jerome@kit.edu> |
| | 171 | * [raw-attachment:master_thesis_ntj.pdf Detailed announcement] |
| | 172 | * Required Skills: Relational model, SQL, Python. Prior knowledge of NoSQL is a plus. |
| | 173 | * Experience Gained: Data management, NoSQL databases, Clustering, Meteorology |
| | 174 | |
| | 175 | To advance the understanding of turbulence and convection in the atmosphere, KIT designed a mobile setup, KITCube, consisting of |
| | 176 | multiple meteorological instruments surveying an atmospheric volume of about 10 km side length. |
| | 177 | These instruments continuously produce large amounts of multi-dimensional time series data, capturing different aspects of |
| | 178 | weather situations in a variety of formats. To understand this phenomena, it is crucial to correlate measurements from different |
| | 179 | instruments with a high spatial and temporal accuracy. Hence, an advance data management and preservation system is required. |
| | 180 | Traditional relational databases have serious limitations in handling multi-dimensional data efficiently but the newly emerged family of NoSQL databases can provide a better approach by its flexible data model, low latency, simple data query and scalability. |
| | 181 | |
| | 182 | You are expected to evaluate different database engines and find an optimal solution to handle the data produced by KITCube, be it |
| | 183 | with NoSQL database or as a hybrid system of relational and non-relational database. You should identify the standard data access |
| | 184 | patterns and benchmark the considered storage engines in each selected scenario. The reliability and scalability in the clustered |
| | 185 | environment have to be taken into account as well. The goal is to create a data storage system for KITCube to handle large archives |
| | 186 | of meteorology data. This work will be carried out in close collaboration of the IPE data management group with the IMK meteorologists. |
| | 187 | |
| | 188 | == Web-Platform for Online Monitoring of Large Scientific Experiments == |
| | 189 | * Contact person: Chuan Miao <chuan.miao@kit.edu> |
| | 190 | * [raw-attachment:1407-master-adei-display.pdf Detailed announcement] |
| | 191 | * Required Skills: The student is expected to be enthusiastic about web application development and command good knowledge of HTML5 and JavaScript. Experiences in popular JavaScript frameworks are welcome but not necessary. Experiences in mobile applications and/or WebCL/WebGL are a plus. |
| | 192 | * Experience Gained: Web application development; Visualization of scientific data; Data management for large scientific facilities |
| | 193 | |
| | 194 | In KIT, many scientific and engineering facilities demand a fast, configurable and yet versatile status |
| | 195 | display. Usually, large amount of data are collected from various devices and sensors at frequent rates. To help scientists and engineers in supervising the experiments, a status display provides a quick overview of device operation and the current |
| | 196 | measurements. |
| | 197 | |
| | 198 | The student is expected to design and implement a web-based platform providing all necessary instruments to quickly build new status displays for a variety of experiments. It should provide rich data visualization capabilities, clean configuration interface, |
| | 199 | and run across major platforms and browsers. One of the challenges is to automatically adapt to the available display resolution supporting client hardware scaling from small mobile devices to the large visualization stations. Fast and interactive visualization of 2D and 3D data is another emphasis of the project. |
