The numerical analysis of ordinary differential equations: Runge-Kutta and general linear methods
The numerical analysis of ordinary differential equations: Runge-Kutta and general linear methods
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Modeling the benefits of mixed data and task parallelism
Proceedings of the seventh annual ACM symposium on Parallel algorithms and architectures
A Programming Methodology for Dual-Tier Multicomputers
IEEE Transactions on Software Engineering - Special issue on architecture-independent languages and software tools for parallel processing
NestStep: Nested Parallelism and Virtual Shared Memory for the BSP Model
The Journal of Supercomputing
Approaches for Integrating Task and Data Parallelism
IEEE Concurrency
A programming model for block-structured scientific calculations on smp clusters
A programming model for block-structured scientific calculations on smp clusters
Multilevel hierarchical matrix multiplication on clusters
Proceedings of the 18th annual international conference on Supercomputing
Work-stealing for mixed-mode parallelism by deterministic team-building
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
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On many parallel target platforms it can be advantageous to implement parallel applications as a collection of multiprocessor tasks that are concurrently executed and are internally implemented with fine-grain SPMD parallelism. A class of applications which can benefit from this programming style are methods for solving systems of ordinary differential equations. Many recent solvers have been designed with an additional potential of method parallelism, but the actual effectiveness of mixed task and data parallelism depends on the specific communication and computation requirements imposed by the equation to be solved. In this paper we study mixed task and data parallel implementations for general linear methods realized using a library for multiprocessor task programming. Experiments on a number of different platforms show good efficiency results.