A bridging model for parallel computation
Communications of the ACM
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
A Framework for Exploiting Task and Data Parallelism on Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
Compiler support for task scheduling in hierarchical execution models
Journal of Systems Architecture: the EUROMICRO Journal - Special issue on tools and environments for parallel program development
A Transformation Approach to Derive Efficient Parallel Implementations
IEEE Transactions on Software Engineering - Special issue on architecture-independent languages and software tools parallel processing
Approaches for Integrating Task and Data Parallelism
IEEE Concurrency
A Low-Cost Approach towards Mixed Task and Data Parallel Scheduling
ICPP '02 Proceedings of the 2001 International Conference on Parallel Processing
CPR: Mixed Task and Data Parallel Scheduling for Distributed Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Approximation Algorithms for Scheduling Malleable Tasks under Precedence Constraints
ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
A $\frac32$-Approximation Algorithm for Scheduling Independent Monotonic Malleable Tasks
SIAM Journal on Computing
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The performance of many scientific applications for distributed memory platforms can be increased by utilizing multiprocessortask programming. To obtain the minimum parallel runtime an appropriate schedule that takes the computation and communication performance of the target platform into account is required. However, many tools and environments for multiprocessor-task programming lack the support for an integrated scheduler. This paper presents a scheduling toolkit, which provides this support and integrates popular scheduling algorithms. The implemented scheduling algorithms provide an infrastructure to automatically determine a schedule for multiprocessor-tasks with dependencies represented by a task graph.