Optimal use of mixed task and data parallelism for pipelined computations
Journal of Parallel and Distributed Computing
A Pipeline-Based Approach for Scheduling Video Processing Algorithms on NOW
IEEE Transactions on Parallel and Distributed Systems
An Analytical Model for Pipeline Algorithms on Heterogeneous Clusters
Proceedings of the 9th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
PEPA nets: a structured performance modelling formalism
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
PDP '05 Proceedings of the 13th Euromicro Conference on Parallel, Distributed and Network-Based Processing
Modeling master/worker applications for automatic performance tuning
Parallel Computing - Algorithmic skeletons
Mapping pipeline skeletons onto heterogeneous platforms
Journal of Parallel and Distributed Computing
Dynamic Pipeline Mapping (DPM)
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Scalable dynamic Monitoring, Analysis and Tuning Environment for parallel applications
Journal of Parallel and Distributed Computing
Modeling pipeline applications in POETRIES
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Automatic tuning of master/worker applications
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Scheduling of DSP programs onto multiprocessors for maximumthroughput
IEEE Transactions on Signal Processing
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We propose to use knowledge about a parallel application's structure that was acquired with the use of a skeleton based development strategy to dynamically improve its performance. Parallel/distributed programming provides the possibility of solving highly demanding computational problems. However, this type of application requires support tools in all phases of the development cycle because the implementation is extremely difficult, especially for non-expert programmers. This work shows a new strategy for dynamically improving the performance of pipeline applications. We call this approach Dynamic Pipeline Mapping (DPM), and the key idea is to have free computational resources by gathering the pipeline's fastest stages and then using these resources to replicate the slowest stages. We present two versions of this strategy, both with complexity O(Nlog(N)) on the number of pipe stages, and we compare them to an optimal mapping algorithm and to the Binary Search Closest (BSC) algorithm [1]. Our results show that the DPM leads to significant performance improvements, increasing the application throughput up to 40% on average.