Algorithmic skeletons: structured management of parallel computation
Algorithmic skeletons: structured management of parallel computation
Pipelined Processor Farms: Structured Design for Embedded Parallel Systems
Pipelined Processor Farms: Structured Design for Embedded Parallel Systems
IEEE Computational Science & Engineering
Towards the automatic optimal mapping of pipeline algorithms
Parallel Computing
Grid programming: some indications where we are headed
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An advanced environment supporting structured parallel programming in Java
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Analyzing Computer Systems Performance: With Perl: Pdq (Springer Professional Computing)
Analyzing Computer Systems Performance: With Perl: Pdq (Springer Professional Computing)
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Self adaptivity in Grid computing: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
A Simple MPI Process Swapping Architecture for Iterative Applications
International Journal of High Performance Computing Applications
Self-adaptive skeletal task farm for computational grids
Parallel Computing - Algorithmic skeletons
An adaptive skeletal task farm for grids
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Macro pipelining based scheduling on high performance heterogeneousmultiprocessor systems
IEEE Transactions on Signal Processing
Adaptive structured parallelism for computational grids
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
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This work describes an adaptive parallel pipeline skeleton which maps pipeline stages to the best processors available in the system and clears dynamically emerging performance bottlenecks at run-time by re-mapping affected stages to other processors. It is implemented in C and MPI and evaluated on a non-dedicated heterogeneous Linux cluster. We report upon the skeleton's ability to respond to an artificially generated variation in the background load across the cluster.