In order to enhance the performance of modern computers, the current development is towards placing multiple cores on one chip instead of inreasing the clock rates. To gain a speed-up from this architecture, software programs have to be partitioned into several independent parts. A common representation of these parts is called a task graph or data dependency graph. The authors of this article have developed a module for the OpenModelica Compiler (OMC), which creates, simplifies and schedules such task graphs. The tasks are created based on the BLT (block lower triangular)structure, which is derived from the right hand side of the model equations. A noticeable speed-up for fluid models on modern six-core CPUs can be achieved.
Large and highly-detailed Modelica models are frequently modeled by utilizing repeated structures, which is a repetition of various elements that are linked together in an iterative manner. While the Modelica language standard supports the representation of repeated structures, most Modelica compilers do not exploit their advantages for efficient simulations. Instead, all repeated equations are flattened and all array variables are expanded. This leads to unnecessarily long compile times and higher memory consumption. Another aspect that has been yet inadequately considered and is closely connected to repeated structures is vectorization. The vector units of modern CPUs can be engaged to perform SIMD (Single Instruction, Multiple Data) operations, executing the same instruction on multiple data points in parallel. This reveals a high potential for faster simulations. This paper discusses the advantages of utilizing repeated structures for modeling in order to achieve both faster compilation and simulation times. The potentials of preserving for loops throughout compilation are demonstrated using a basic implementation in the OpenModelica Compiler. The effect on the simulation time by enabling vectorization is demonstrated for an appropriate model.
Absfraci-Efticient navigation of mobile platforms in dynamic, human centered environments is still an open research topic. We have already proposed an architecture (MEPHISTO) for a navigation system that is able to fulfill the main requirements of efficient navigation: fast and reliable seusor processing, extensive global world modeling and distributed path planning. Our architecture uses a distributed system of sensor processing, world modeling and path planning units. In this paper we present some implemented methods in the contest of dynamic object detection and global and mobile sensor data fusion for 3D world modeling. Experimental results of the system in the laboratory environment are presented. Index ferms4ata fusion, navigation, distributed vision, binary space partition, 3D laser range scanner
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