| | 44 | == Web-based monitoring of large-scale data in scientific experiments == |
| | 45 | * Contact person: Suren A. Chilingaryan csa@dside.dyndns.org |
| | 46 | * [raw-attachment:1301-adei-status-v2.pdf Detailed announcement] |
| | 47 | * Required Skills: JavaScript & PHP; knowledge of OpenGL/WebGL is a plus |
| | 48 | * Experience Gained: WebCL/WebGL, Data management in high energy physics experiments, Visualization of scientific data |
| | 49 | Huge quantities of information are produced by scientific experiments world wide. Data formats, underlying storage engines, and sampling rates are varying significantly. At the Institute for Data Processing and Electronics we develop a web-based visualization framework which handles multiple types of slow-control data and helps engineers and scientists to inspect device operation and examine the integrity and validity of the measurements. The framework is used in a wide area of applications ranging from fusions experiments, astroparticle physics, to meteorological systems. |
| | 50 | State-of-the-art web browsers support a rich set of features to construct sophisticated interfaces using web technologies only. With introduction of WebGL it become possible to perform 3D visualization as well. |
| | 51 | The student is expected to design and implement a new module for real-time monitoring. The main challenge is to visualize multi-dimensional data sets and arrays of sensors mapped to the 3D models. One example is shown in the image below where an array of temperature sensors was mapped to the model of KATRIN tank to visualize the temperature distribution. |
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| | 53 | == Optimizing imaging algorithms to the latest parallel CPU and GPU architectures == |
| | 54 | * Contact person: Suren A. Chilingaryan csa@dside.dyndns.org |
| | 55 | * [raw-attachment:1301-adei-status-v2.pdf Detailed announcement] |
| | 56 | * Required Skills: Good knowledge of C programming language, knowledge of OpenCL or/and CUDA is a plus |
| | 57 | * Experience Gained: Parallel programming, GPU programming, Image processing |
| | 58 | 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. |
| | 59 | 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. |
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