<i>Reengineering for Parallelism</i>: an entry point into PLPP for legacy applications

Massingill, Berna L.; Mattson, Timothy G.; Sanders, Beverly A.

doi:10.1002/cpe.1147

Cited by 14 publications

(10 citation statements)

References 9 publications

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“…Another participant remarked that no single consideration is most important for a good data decomposition, reinforcing the sentiment expressed by Massingill et al [8] (see Section I) who argued it is a balance of competing forces: "I rank everything as a top priority. I guess the point is that when jumping a canyon one needs to get 100% of the way across, 20%, 50%, and 90% just don't cut it.…”

Section: ) General Parallelization Findingsmentioning

confidence: 65%

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Data Decomposition for Code Parallelization in Practice: What Do the Experts Need?

Meade

Deeptimahanti

Johnston

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

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Abstract-Parallelizing serial software systems in order to run in a High Performance Computing (HPC) environment presents many challenges to developers. In particular, the extant literature suggests the task of decomposing large-scale data applications is particularly complex and time-consuming. In order to take stock of the state of practice of data decomposition in HPC, we conducted a two-phased study. Firstly, using focus group methodology we conducted an exploratory study at a software laboratory with an established track record in HPC. Based on the findings of this first phase, we designed a survey to assess the state of practice among experts in this field around the world. Our study shows that approximately 75% of parallelized applications use some form of data decomposition. Furthermore, data decomposition was found to be the most challenging phase in the parallelization process, consuming approximately 40% of the total time. A key finding of our study is that experts do not use any of the available tools and formal representations, and in fact, are not aware of them. We discuss why existing tools have not been adopted in industry and based on our findings, provide a number of recommendations for future tool support.

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Section: ) General Parallelization Findingsmentioning

confidence: 65%

“…These tasks have been characterized as extremely complex and errorprone [7]. Massingill et al [8] emphasize the complexity involved, arguing that, "A good data decomposition balances the competing forces of flexibility, efficiency, and simplicity. "…”

Section: Introductionmentioning

confidence: 99%

Data Decomposition for Code Parallelization in Practice: What Do the Experts Need?

Meade

Deeptimahanti

Johnston

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

View full text Add to dashboard Cite

show abstract

“…Sutter emphasised the need to change development practice and incorporate parallelization. However, the task of parallelization is not an easy feat as Massingill et al [10] emphasise:…”

Section: Background and Motivationmentioning

confidence: 98%

“…Three papers reported this challenge. A data dependence results from multiple use of the same location(s) in storage by different tasks, for example one statement produces a [10,18] result and saves it in a location, and a second statement references that result by looking up that location. To perform work effectively, each calculation needs to have access to data that it needs.…”

Section: Data Dependencies -C7mentioning

confidence: 99%

Challenges of evolving sequential to parallel code

Meade

Buckley

Collins

2011

Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th Annual ERCIM Workshop on Softwar

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Large legacy systems that have been in use for several decades need to evolve in order to take advantage of new technological advances. One such development is the emergence of multi-core processors and parallel platforms. However, the evolution of code written for single-core platforms into code that can take advantage of multi-core technology is challenging. The aim of this research is to explore the challenges that parallel programmers face in the evolution of existing software to exploit multicore and parallel architectures. A review of the current literature was conducted and ten frequently reported challenges were identified. It is important to raise awareness of potential challenges that practitioners may face when evolving sequential code to exploit multicore platforms in order to be better prepared for future evolution. The research community can use these results to develop a research agenda in order to design and develop solutions to address these challenges.

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“…A pattern language for parallel computing (Mattson et al, 2004;Massingill et al, 2007) is introduced to exploit the possibilities for parallelization in the coupled MAS model and environmental model. Specifically, the coupled MAS model and environmental model fit naturally to the agent and repository structural pattern defined by Our Pattern Language (OPL; Keutzer and Mattson, 2009).…”

Section: Agent and Repository Structural Patternmentioning

confidence: 99%