Raising the level of abstraction for developing message passing applications

Arora, Ritu; Bangalore, Purushotham; Mernik, Marjan

doi:10.1007/s11227-010-0490-3

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…The Parallel Application Visual Modling (PAVM) toolset is based on DSL tools and supported by a model checker and code generator. Arora et al [49] define an approach to develop message passing applications. They developed generative programming tools with a domain-specific language, named Hi-PaL.…”

Section: Related Workmentioning

confidence: 99%

ParDSL: a domain-specific language framework for supporting deployment of parallel algorithms

Tekinerdoğan

Arkin

2018

Softw Syst Model

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An important challenge in parallel computing is the mapping of parallel algorithms to parallel computing platforms. This requires several activities such as the analysis of the parallel algorithm, the definition of the logical configuration of the platform and the implementation and deployment of the algorithm to the computing platform. However, in current parallel computing approaches very often only conceptual and idiosyncratic models are used which fall short in supporting the communication and analysis of the design decisions. In this article, we present ParDSL, a domain-specific language framework for providing explicit models to support the activities for mapping parallel algorithms to parallel computing platforms. The language framework includes four coherent set of domain-specific languages each of which focuses on an activity of the mapping process. We use the domain-specific languages for modeling the design as well as for generating the required platformspecific models and the code of the selected parallel algorithm. In addition to the languages, a library is defined to support systematic reuse. We discuss the overall architecture of the language framework, the separate DSLs, the corresponding model transformations and the toolset. The framework is illustrated for four different parallel computing algorithms.

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Section: Related Workmentioning

confidence: 99%

ParDSL: a domain-specific language framework for supporting deployment of parallel algorithms

Tekinerdoğan

Arkin

2018

Softw Syst Model

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“…They can select cyclic or block-cyclic schemes too. The design templates in IPT are reusable across a diverse range of applications -e.g., the templates for inter-process communication and data-distribution for stencil-based computations can be used for parallelizing Poisson's Solver as well as a trivial Game of Life problem [10]. The run-time comparison of the manually written code and the generated code for the test cases presented in this section is shown in Figures 11, 12, and 13.…”

Section: Figure 6 Snippet Of Circuit Satisfiability Code -Serialmentioning

confidence: 99%

“…The IPT has a subset of functionality that is available in our framework for parallelization known as FraSPA [10,11], and is adapted for light-weight usage through a wizard-based GUI and a command-line interface. The latest additions to FraSPA include support for parallel programming through a Graphical User Interface (GUI), and the support for generating CUDA programs.…”

Section: Introductionmentioning

confidence: 99%

A Tool for Interactive Parallelization

Arora¹,

Olaya

Gupta³

2014

Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment

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Tell me, I'll forget, Show me, I may remember, Involve me, I'll understand." -Confucius This proverb describes the essence of our paper and the motivation behind the development of the Interactive Parallelization Tool (IPT) that can transform serial applications into multiple parallel variants. The end-users of IPT must have an understanding of the basic concepts involved in parallel programming (e.g., data distribution and data gathering). After developing an understanding of the basic parallel programming concepts, IPT can be used by its target audience (domain-experts and students) to semi-automatically generate parallel programs using multiple parallel programming paradigms (MPI, OpenMP, and CUDA), and learn about these paradigms through observation and comparison. This IPT-based personalized learning approach complements the traditional methods of learning and training that usually emphasize the syntax and semantics of one or more programming standards. The main benefit of IPT is that it provides a jumpstart to the domain-experts in using modern HPC platforms for their research and development needs, and hence lowers the adoption barriers to HPC.

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“…As TACO uses a global address space data distribution, it is therefore not useful in a purely distributed memory environment. [24] is part of a larger framework called FraSPA (A framework for Synthesizing Parallel Applications). This work involves the creation of a domain specific language (DSL) called Hi-PaL (High Level Parallelization Language) with the aim of reducing the effort involved in writing parallel applications by using a higher level of abstraction than MPI.…”

Section: Background and Motivationmentioning

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

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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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Raising the level of abstraction for developing message passing applications

Cited by 11 publications

References 29 publications

ParDSL: a domain-specific language framework for supporting deployment of parallel algorithms

ParDSL: a domain-specific language framework for supporting deployment of parallel algorithms

A Tool for Interactive Parallelization

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

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