Data parallel programming provides an accessible model for exploiting the power of parallel computing elements without resorting to the explicit use of low level programming techniques based on locks, threads and moni...
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The development of real-time systems is one of the areas with the highest relevance in computer science, and the number of critical systems has increased significantly. These systems considers several applications run...
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In recent years, the research community has made great strides in alias annotations that support parallel programming [1]. Using these techniques, programmers no longer have to guess where aliased mutable state may ca...
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FastFlow is an open source, structured parallel programming framework originally conceived to support highly efficient stream parallel computation while targeting shared memory multi cores. Its efficiency mainly comes...
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While OpenMP is the de facto standard of shared memory parallel programming models, a number of alternative programming models and runtime systems have arisen in recent years. Fairly evaluating these programming syste...
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The use of a Phase Field method for medical image segmentation is proposed in this paper. The Allen-Cahn equation, a mathematical model equation, is used in this method. The Finite Difference method is used for numeri...
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The use of a Phase Field method for medical image segmentation is proposed in this paper. The Allen-Cahn equation, a mathematical model equation, is used in this method. The Finite Difference method is used for numerical discretization of model equations and semi-algebraic equations integrated over time using the second-order Runge-Kutta method. Numerical algorithms are implemented into computer programming using the serial and parallel C programming language based on GPU CUDA. Based on image segmentation calculations, the Phase Field method has high accuracy. It is indicated by the Jaccard Index and Dice Similarity values that are close to one. The range of Jaccard Index values is 0.859 - 0.952, while the Dice Similarity value range is 0.926 - 0.976. In addition, it is shown that parallel programming with GPU CUDA can accelerate 45.72 times compared to serial programming.
Mesh free parallel implementations using multi-core processors are discussed. Particularly, parallelization of the MLPG and SPEM are shown. The appropriate strategies to correctly parallelize the mesh free methods are...
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Today, almost every computer has at least one multicore processor. To remain in stride with hardware developments, numerous university faculties oriented towards computer science have introduced parallel programming a...
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Today, almost every computer has at least one multicore processor. To remain in stride with hardware developments, numerous university faculties oriented towards computer science have introduced parallel programming as an integral part of their courses. The question is, given the availability of parallel architectures, and considering future trends in programming, whether it is time for parallel programming to also become an integral part of the informatics curriculum in secondary schools? This paper presents research conducted in three schools in Croatia over several school years. A total of 162 students from the science-mathematic high schools participated in the research. The results, based on student evaluations, suggest that this course content is equally interesting and somewhat more difficult, and perceived as equally useful as other course content taught to students. Moreover, the findings indicate that students can understand and later apply some of the fundamental concepts of parallel programming.
The framework of an introductory course in parallel programming for third-year ***. students in the field of software engineering is discussed. The mandatory course will be taught at the Shenkar College of Engineering...
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The framework of an introductory course in parallel programming for third-year ***. students in the field of software engineering is discussed. The mandatory course will be taught at the Shenkar College of Engineering and Design, Israel, and covers both theoretical sessions and laboratory training. The novel course will provide lecturers the flexibility to choose the topics they think are more important and decide whether to emphasize laboratory work or theoretical lectures. The course covers several theoretical exercises such as think parallel, parallel architectures, design or parallel applications, performance and metrics, parallel algorithms, parallel programming models and paradigms, mapping and scheduling, and parallel databases. The training sessions in the lab are devoted to providing experience in writing parallel code including configuration, compilation, running, debugging, and evaluation of performance and results correctness.
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