http://dl.gi.de/handle/20.500.12116/1910 2026-07-21T14:15:05Z http://dl.gi.de/handle/20.500.12116/1929 Parallelization of the Particle-in-cell-Code PATRIC with GPU-Programming Fitzek, Jutta The Particle-in-cell (PIC) code PATRIC (Particle Tracking Code) is used at the GSI Helmholtz Center for Heavy Ion Reasearch to simulate particles in circular particle accelerators. Parallelization of PIC codes is an open research field and solutions depend very much on the specific problem. The possibilities and limits of GPU integration are being evaluated. General GPU aspects and problems arising from collective particle effects are put into focus with an emphasis on code maintainability and reuse of existing modules. The studies have been performed using NVIDIA⃝R ’s Tesla C2075 GPU. This contribution summarizes the findings. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/1930 Real-Time Vision System for License Plate Detection and Recognition on FPGA Rosli, Faird; Elhossini, Ahmed; Juurlink, Ben Rapid development of the Field Programmable Gate Array (FPGA) offers an alternative way to provide acceleration for computationally intensive tasks such as digital signal and image processing. Its ability to perform parallel processing shows the potential in implementing a high speed vision system. Out of numerous applications of computer vision, this paper focuses on the hardware implementation of one that is commercially known as Automatic Number Plate Recognition (ANPR).Morphological operations and Optical Character Recognition (OCR) algorithms have been implemented on a Xilinx Zynq-7000 All-Programmable SoC to realize the functions of an ANPR system. Test results have shown that the designed and implemented processing pipeline that consumed 63 % of the logic resources is capable of delivering the results with relatively low error rate. Most importantly, the computation time satisfies the real-time requirement for many ANPR applications. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/1927 Particle-in-Cell algorithms on DEEP: The iPiC3D case study Jakobs, Anna; Zitz, Anke; Eicker, Norbert; Lapenta, Giovanni The DEEP (Dynamical Exascale Entry Platform) project aims to provide a first implementation of a novel architecture for heterogeneous high-performance computing. This architecture consists of a standard HPC Cluster and – tightly coupled – a cluster of many-core processors called Booster. This concept offers application developers the opportunity to run different parts of their program on the best fitting part of the machine striving for an optimal overall performance. In order to take advantage of this architecture applications require some adaption. To provide optimal support to the application developers the DEEP concept includes a high-level programming model that helps to separate a given program to the Cluster and Booster parts of the DEEP System. This paper presents the adaption work required for a Particle-in-Cell space weather application developed by KULeuven (Katholieke Universiteit Leuven) done in the course of the DEEP project. It discusses all crucial steps of the work starting with a scalability analysis of the different parts of the program, their performance projections for the Cluster and the Booster leading to the separation decisions for the application and finally the actual implementation work. In addition to that some performance results are presented. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/1931 Extended Pattern-based Parallelization Approach for Hard Real-Time Systems and its Tool Support Stegmeier, Alexander; Frieb, Martin; Ungerer, Theo The transformation of sequential legacy code to parallel applications is hard, especially when timing requirements have to be met. There exists a systematic parallelization approach dealing with this topic. Based on practical experience, we extend it and present our modifications. Our extensions comprise an additional phase dealing with implementation details and another one for quality assurance. Its results may be used to further improve the parallel program. Moreover, we propose tool support which further facilitates the parallelization process. 2015-01-01T00:00:00Z