Data parallel languages such as Vienna Fortran and HPF can be successfully applied to a wide range of numerical applications. However, many advanced scientic and engineering appl...
Barbara M. Chapman, Piyush Mehrotra, John Van Rose...
Abstract. This paper presents a novel proposal to define task parallelism in OpenMP. Task parallelism has been lacking in the OpenMP language for a number of years already. As we ...
OpenMP is widely used for shared memory parallel programming and is especially useful for the parallelisation of loops. When it comes to task parallelism, however, OpenMP is less p...
Oliver Sinnen, Jsun Pe, Alexander Vladimirovich Ko...
Parallel computing is notoriously challenging due to the difficulty in developing correct and efficient programs. With the arrival of multi-core processors for desktop systems, ...
Multiple programming models are emerging to address an increased need for dynamic task parallelism in applications for multicore processors and shared-address-space parallel compu...
Yi Guo, Rajkishore Barik, Raghavan Raman, Vivek Sa...
Through the algorthmic design patterns of data parallelism and task parallelism, the graphics processing unit (GPU) offers the potential to vastly accelerate discovery and innovat...
Jeremy S. Archuleta, Yong Cao, Thomas Scogland, Wu...
—Multicore machines are becoming common. There are many languages, language extensions and libraries devoted to improve the programmability and performance of these machines. In ...
Diego Andrade, Basilio B. Fraguela, James C. Brodm...
We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parall...
Mohammed Javeed Zaki, Ching-Tien Ho, Rakesh Agrawa...