Global Optimization for Mapping Parallel Image Processing Tasks on Distributed Memory Machines

Lee, Cheolwhan; Wang, Yuan-Fang; Yang, Tao

doi:10.1006/jpdc.1997.1360

Cited by 18 publications

(11 citation statements)

References 28 publications

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“…Finally, the approach may guarantee optimal performance of sequences of library routines, but not necessarily of complete programs. It should be noted that the approach of Lee et al [15] is quite similar to that of Morrow et al; as a consequence, it suffers from the very same problems as well.…”

Section: Related Workmentioning

confidence: 92%

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Seinstra

Koelma

Bagdanov

2004

IEEE Trans. Parallel Distrib. Syst.

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A popular approach to providing nonexperts in parallel computing with an easy-to-use programming model is to design a software library consisting of a set of preparallelized routines, and hide the intricacies of parallelization behind the library's API. However, for regular domain problems (such as simple matrix manipulations or low-level image processing applications-in which all elements in a regular subset of a dense data field are accessed in turn) speedup obtained with many such library-based parallelization tools is often suboptimal. This is because interoperation optimization (or: time-optimization of communication steps across library calls) is generally not incorporated in the library implementations. This paper presents a simple, efficient, finite state machine-based approach for communication minimization of library-based data parallel regular domain problems. In the approach, referred to as lazy parallelization, a sequential program is parallelized automatically at runtime by inserting communication primitives and memory management operations whenever necessary. Apart from being simple and cheap, lazy parallelization guarantees to generate legal, correct, and efficient parallel programs at all times. The effectiveness of the approach is demonstrated by analyzing the performance characteristics of two typical regular domain problems obtained from the field of low-level image processing. Experimental results show significant performance improvements over nonoptimized parallel applications. Moreover, obtained communication behavior is found to be optimal with respect to the abstraction level of message passing programs.

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Section: Related Workmentioning

confidence: 92%

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Seinstra

Koelma

Bagdanov

2004

IEEE Trans. Parallel Distrib. Syst.

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“…In this sense, we have analyzed different studies focused on distributing tiles between MPI processes. Some of them were based on improving the speedup of the parallel applications [17] [18] and others that are focused on enhancing the computational efficiency on multi-core clusters [19], [20]. However, we are including in our method a mapping technique that can combine both performance metrics (speedup and efficiency).…”

Section: Related Workmentioning

confidence: 99%

An approach for an efficient execution of SPMD applications on Multi-core environments

Muresano

Meyer

Rexachs

et al. 2017

Future Generation Computer Systems

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Executing traditional Message Passing Interface (MPI) applications on multi-core cluster balancing speed and computational efficiency is a difficult task that parallel programmers have to deal with. For this reason, communications on multi-core clusters ought to be handled carefully in order to improve performance metrics such as efficiency, speedup, execution time and scalability. In this paper we focus our attention on SPMD (Single Program Multiple Data) applications with high communication volume and synchronicity and also following characteristics such as: static, local and regular. This work proposes a method for SPMD applications, which is focused on managing the communication heterogeneity (different cache level, RAM memory, network, etc.) on homogeneous multi-core computing platform in order to improve the application efficiency. In this sense, the main objective of this work is to find analytically the ideal number of cores necessary that allows us to obtain the maximum speedup, while the computational efficiency is maintained over a defined threshold (strong scalability). This method also allows us to determine how the problem size must be increased in order to maintain an execution time constant while the number of cores are expanded (weak scalability) considering the tradeoff between speed and efficiency. This methodology has been tested with different benchmarks and applications and we achieved an average improvement around 30.35% of efficiency in applications tested using different problems sizes and multi-core clusters. In addition, results show that maximum speedup with a defined efficiency is located close to the values calculated with our analytical model with an error rate lower than 5% for the applications tested.This research has been supported by the MINECO (MICINN) Spain under contracts TIN2011-24384 and TIN2014-\ud 53172-PPeer ReviewedPostprint (author's final draft

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“…Other programming models have been applied to image processing, including, for example, a number dealing with the integration of task and data parallelism [11][12][13][14] and the mapping of image processing tasks on to distributed memory machines [15][16][17]. Downton has also proposed a pipeline processor farm (PPF) as a general design method to support parallelisation and has applied this to image processing applications using MIMD processors [18,19].…”

Section: Introductionmentioning

confidence: 98%

Efficient implementation of a portable parallel programming model for image processing

Morrow

Crookes

Brown

et al. 1999

Concurrency: Pract. Exper.

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Global Optimization for Mapping Parallel Image Processing Tasks on Distributed Memory Machines

Cited by 18 publications

References 28 publications

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

An approach for an efficient execution of SPMD applications on Multi-core environments

Efficient implementation of a portable parallel programming model for image processing

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