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The main computing phases of numerical methods for solving partial differential equations are the algebraic system assembly and the iterative solver. This work focuses on the first task, in the context of a hybrid MPI+X paradigm. The matrix assembly consists of a loop over the elements, faces, edges or nodes of the MPI partitions to compute element matrices and vectors and then of their assemblies. In a MPI+X hybrid parallelism context, X has consisted traditionally of loop parallelism using OpenMP, with different techniques to avoid the race condition, but presenting efficiency or implementation drawbacks. We propose an alternative, based on task parallelism using some extensions to the OpenMP programming model. In addition, dynamic load balance will be applied, especially efficient in the presence of hybrid meshes. This paper presents the proposed methodology, its implementation and its validation through the solution of large computational mechanics problems up to 16k cores. [ABSTRACT FROM AUTHOR]

In this paper, we present OmpSs, a programming model based on OpenMP and StarSs, that can also incorporate the use of OpenCL or CUDA kernels. We evaluate the proposal on different architectures, SMP, GPUs, and hybrid SMP/GPU environments, showing the wide usefulness of the approach. The evaluation is done with six different benchmarks, Matrix Multiply, BlackScholes, Perlin Noise, Julia Set, PBPI and FixedGrid. We compare the results obtained with the execution of the same benchmarks written in OpenCL or OpenMP, on the same architectures. The results show that OmpSs greatly outperforms both environments. With the use of OmpSs the programming environment is more flexible than traditional approaches to exploit multiple accelerators, and due to the simplicity of the annotations, it increases programmer's productivity. [ABSTRACT FROM AUTHOR]

This paper describes the support provided by the NanosCompiler to nested parallelism in OpenMP. The NanosCompiler is a source-to-source parallelizing compiler implemented around a hierarchical internal program representation that captures the parallelism expressed by the user (through OpenMP directives and extensions) and the parallelism automatically discovered by the compiler through a detailed analysis of data and control dependences. The compiler is finally responsible for encapsulating work into threads, establishing their execution precedences and selecting the mechanisms to execute them in parallel. The NanosCompiler enables the experimentation with different work allocation strategies for nested parallel constructs. Some OpenMP extensions are proposed to allow the specification of thread groups and precedence relations among them. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]